From 30847598411c59d430083748327d218e4392717e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 03:11:14 +0000 Subject: [PATCH 01/26] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20SRFBN=5Ffor=5FTensor?= =?UTF-8?q?Flow?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/.keep diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/.keep b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 95bf83e17801b71ed53d439e043cf4258adabc95 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 03:11:52 +0000 Subject: [PATCH 02/26] =?UTF-8?q?=E6=8F=90=E4=BA=A4=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/Basic_Model.py | 144 +++++ .../contrib/cv/SRFBN_for_TensorFlow/LICENSE | 284 ++++++++++ .../cv/SRFBN_for_TensorFlow/PreProcess.py | 70 +++ .../contrib/cv/SRFBN_for_TensorFlow/README.md | 118 ++++ .../cv/SRFBN_for_TensorFlow/SRFBN_model.py | 181 +++++++ .../contrib/cv/SRFBN_for_TensorFlow/config.py | 53 ++ .../cv/SRFBN_for_TensorFlow/psnr_ssim.py | 86 +++ .../cv/SRFBN_for_TensorFlow/requirements.txt | 4 + .../contrib/cv/SRFBN_for_TensorFlow/test.py | 61 +++ .../SRFBN_for_TensorFlow/traditional_blur.py | 508 ++++++++++++++++++ .../contrib/cv/SRFBN_for_TensorFlow/train.py | 124 +++++ 11 files changed, 1633 insertions(+) create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/LICENSE create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/requirements.txt create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py new file mode 100644 index 000000000..e902cf5b6 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py @@ -0,0 +1,144 @@ +from npu_bridge.npu_init import * +import tensorflow as tf +from tensorflow.contrib import layers +import sys + +class basic_network(object): + #初始化网络配置 + def __init__(self, cfg): + self.training=True + self.cfg = cfg + self.params_count = 0#参数数量 + #初始化参数 + def init_params(self, *args, **kwargs): + def _variable_on_cpu(w_shape, b_shape, weight_decay=0.99, use_bias=True, name="conv"): + with tf.device('/cpu:0'):#使用cpu + w = tf.Variable(tf.truncated_normal(w_shape, 0.0, 0.001), trainable=True, name="%s_w" % name)#权重 + tf.add_to_collection(name="weights_l2_loss", value=self.calc_l1_loss(w, weight_decay))#将value加入name为'weights_l2_loss'的列表中 + b = tf.Variable(tf.zeros(b_shape), trainable=use_bias, name="%s_b" % name)#偏置 + return w, b #返回参数w和b + kernel_size = kwargs["kernel_size"]#kwargs为字典 + in_channels = kwargs["in_channels"] + out_channels = kwargs["out_channels"] + # weight_decay = kwargs["weight_decay"] + w_shape = [kernel_size, kernel_size, in_channels, out_channels]#权重的size + b_shape = [out_channels]#b的size + name = kwargs["name"] + self.params_count += kernel_size*kernel_size*in_channels*out_channels + self.params_count += out_channels#参数的数量 + return _variable_on_cpu(w_shape, b_shape, use_bias=kwargs["use_bias"], name=name)#返回初始化后的w和b + #计算代价函数，L1和L2 + def calc_loss(self, *args, **kwargs): + loss_type = kwargs["loss_type"] + x = kwargs["x"] + y = kwargs["y"] + if loss_type == "L1":#代价函数类型 + return tf.reduce_sum(tf.abs(x-y), name="L1_loss") + elif loss_type == "L2": + return tf.nn.l2_loss((x-y), name="L2_loss") + #激活函数 + def activation(self, *args, **kwargs): + act_type = kwargs["act_type"] + act_type = act_type.lower() + if act_type == "relu": + return tf.nn.relu(args[0]) + elif act_type == "lrelu": + slope = kwargs["slope"] + y = slope*args[0] + return tf.maximum(args[0], y) + elif act_type == "prelu": + return tf.nn.leaky_relu(args[0], alpha=0.2) + elif act_type == "tanh": + return tf.nn.tanh(args[0]) + else: + return args[0] + #计算L2型代价函数 + def calc_l2_loss(self, weight, weight_decay): + _, _, _, outchannel = weight.get_shape().as_list()#这里只需要获取输出的channel数 + return (weight_decay) * tf.reduce_sum(tf.square(weight)) / outchannel + #计算L1型代价函数 + def calc_l1_loss(self, weight, weight_decay): + _, _, _, outchannel = weight.get_shape().as_list() + return (weight_decay)*tf.reduce_sum(tf.abs(weight)) / outchannel + #批归一化处理 + def batch_norm(self, *args, **kwargs): + return tf.layers.batch_normalization(args[0], training=kwargs["training"])#第一个参数是输入 + #归一化的一种方法 + def instance_norm(self, *args, **kwargs): + return layers.instance_norm(args[0], kwargs["name"]) + #激活函数的一种 + def hard_sigmoid(self, x): + return tf.nn.relu6((x+3)/6) + + def hard_swish(self, x): + return x * self.hard_sigmoid(x) + #平均池化 + def global_average_pooling(self, x, name="GAP"): + return tf.reduce_mean(x, axis=[1, 2], keep_dims=True, name="Global_Average_Pooling_%s" % name)#不降维 + + #定义卷积块 + def ConvBlock(self,x, in_channels, out_channels, kernel_size, stride=1, name="ConvBlock", + BN=True, use_bias=True, padding="VALID", act_type="relu", mode="CNA"): + + + assert (mode in ['CNA', 'NAC']), '[ERROR] Wrong mode in [%s]!' % sys.modules[__name__]#断言 + weight, bias = self.init_params(kernel_size=kernel_size, in_channels=in_channels, + out_channels=out_channels, use_bias=use_bias, name=name) + if mode == "CNA":#先卷积再归一化再激活 + x = tf.nn.conv2d(x, filter=weight, strides=[1, stride, stride, 1], padding=padding) + x = tf.nn.bias_add(x, bias) + if BN: + if self.cfg.BN_type == "BN": + x = self.batch_norm(x, training=self.cfg.istrain) + elif self.cfg.BN_type == "IN": + x = self.instance_norm(x, name="%s_IN"%name) + else: + raise NotImplementedError('[ERROR] BN type [%s] is not implemented!' % self.cfg.BN_type) + x = self.activation(x, act_type=act_type) + return x + elif mode=="NAC": + if BN: + if self.cfg.BN_type == "BN": + x = self.batch_norm(x, training=self.cfg.istrain) + elif self.cfg.BN_type == "IN": + x = self.instance_norm(x, name="%s_IN" % name) + else: + raise NotImplementedError('[ERROR] BN type [%s] is not implemented!' % self.cfg.BN_type) + x = self.activation(x, act_type=act_type) + x = tf.nn.conv2d(x, filter=weight, strides=[1, stride, stride, 1], padding=padding) + x = tf.nn.bias_add(x, bias) + return x + #反卷积块（上采样upsampling） + def DeConvBlock(self, x, in_channels, out_channels, kernel_size, stride=1, name="DeConvBlock", + BN=True, use_bias=True, padding="VALID", act_type="relu", mode="CNA"): + assert (mode in ['CNA', 'NAC']), '[ERROR] Wrong mode in [%s]!' % sys.modules[__name__] + b, h, w, c = x.get_shape().as_list()#h为图像高度，w为宽度，b为batch，即一次处理的样本数，c为颜色通道数 + out_shape = [b, h * self.cfg.scale, w * self.cfg.scale, out_channels] + weight, bias = self.init_params(kernel_size=kernel_size, in_channels=out_channels, + out_channels=in_channels, use_bias=use_bias, name=name) + if mode == "CNA":#反卷积函数（扩大图像尺寸）（其实也是卷积） + x = tf.nn.conv2d_transpose(x, filter=weight, output_shape=out_shape, + strides=[1, stride, stride, 1], padding=padding) + x = tf.nn.bias_add(x, bias) + if BN: + if self.cfg.BN_type == "BN": + x = self.batch_norm(x, training=True) + elif self.cfg.BN_type == "IN": + x = self.instance_norm(x, name="%s_IN" % name) + else: + raise NotImplementedError('[ERROR] BN type [%s] is not implemented!' % self.cfg.BN_type) + x = self.activation(x, act_type=act_type) + return x + elif mode == "NAC": + if BN: + if self.cfg.BN_type == "BN": + x = self.batch_norm(x, training=True) + elif self.cfg.BN_type == "IN": + x = self.instance_norm(x, name="%s_IN" % name) + else: + raise NotImplementedError('[ERROR] BN type [%s] is not implemented!' % self.cfg.BN_type) + x = self.activation(x, act_type=act_type) + x = tf.nn.conv2d_transpose(x, filter=weight, output_shape=out_shape, + strides=[1, stride, stride, 1], padding=padding) + x = tf.nn.bias_add(x, bias) + return x diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/LICENSE b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/LICENSE new file mode 100644 index 000000000..5ea8a5f7b --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/LICENSE @@ -0,0 +1,284 @@ + Apache License + Version 2.0, January 2004 + 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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT +HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED +WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE +GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER +IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN +IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py new file mode 100644 index 000000000..c38999b3c --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py @@ -0,0 +1,70 @@ +from npu_bridge.npu_init import * +import cv2 +import numpy as np +import random +#from skimage import util + +#给图像数据添加噪声 +#def add_noise(img): + # mode_types = ['gaussian', 'localvar', 'poisson', 'speckle'] # 'salt', 'pepper', 's&p'这三个噪声太假了 + # inx = int(np.random.choice(np.arange(len(mode_types)), 1)) + # # inx = 0 + # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)#转换色彩空间为RGB + # mean = random.random() * 0.001 # + 0.001#random.random()生成0到1之间的随机数 + # var = random.random() * 0.002 # + 0.01 + # noise_img = util.random_noise(img.copy(), mode=mode_types[inx], + # mean=mean, + # var=var)#添加噪声 + # return noise_img + +#数据扩充或增强 +def augment_data(img_patch, flip, rot): # img_patchs : n,h,w,c + if flip==1: + img_patch = img_patch[:, ::-1, :] # hflip#水平翻转 + elif flip==2: + img_patch = img_patch[::-1, :, :] # vflip#垂直翻转 + if rot==1: + img_patch = cv2.rotate(img_patch, cv2.ROTATE_90_CLOCKWISE)#顺时针旋转90 + elif rot==2: + img_patch = cv2.rotate(img_patch, cv2.ROTATE_90_COUNTERCLOCKWISE) + return img_patch +#预处理数据 +def preprocess(imgs, cfg): + LR_patchs, HR_patchs = [], [] + for img in imgs: + + HR = cv2.imread(img.strip(), cv2.IMREAD_COLOR)#读取图片路径，并以RGB模式 + HR = (HR - 127.5) / 128#归一化 + h, w, c = HR.shape#高度，宽度，颜色通道数 + + x_stride = w // (cfg.imagesize * cfg.scale) + y_stride = h // (cfg.imagesize * cfg.scale) + + for x in range(x_stride): + for y in range(y_stride): + HR_patch = HR[y * cfg.imagesize * cfg.scale:(y + 1) * cfg.imagesize * cfg.scale, + x * cfg.imagesize * cfg.scale:(x + 1) * cfg.imagesize * cfg.scale, :] + # add noise && add blur + t = np.random.randint(0, 2, 1) + if t == 0: + LR_patch = cv2.resize(HR_patch, dsize=None, fx=1 / cfg.scale, fy=1 / cfg.scale, + interpolation=cv2.INTER_CUBIC) + LR_patch = np.clip(LR_patch, -1.0, 1.0) + #LR_patch = add_noise(LR_patch) + else: + #LR_patch = add_noise(HR_patch) # [-1, 1] + LR_patch = cv2.resize(HR_patch, dsize=None, fx=1 / cfg.scale, + fy=1 / cfg.scale, interpolation=cv2.INTER_LINEAR) + # data augment + if cfg.istrain: + rot = np.random.randint(0, 3, 1) + flip = np.random.randint(0, 3, 1) + LR_patch = augment_data(LR_patch, flip, rot) + HR_patch = augment_data(HR_patch, flip, rot) + LR_patchs.append(LR_patch) + HR_patchs.append(HR_patch) + + return HR_patchs, LR_patchs + + + diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md new file mode 100644 index 000000000..79c405514 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md @@ -0,0 +1,118 @@ +- [基本信息](#基本信息.md) +- [概述](#概述.md) +- [训练环境准备](#训练环境准备.md) +- [快速上手](#快速上手.md) +- [训练结果](#训练结果.md) +- [高级参考](#高级参考.md) +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：Computer Vision** + +**修改时间（Modified） ：2022.11.6** + +**框架（Framework）：TensorFlow 1.15.0** + +**描述（Description）：基于TensorFlow框架对高清图片重建相应的超分辨率图片的训练代码** + +

概述

+ +``` +SRFBN是采取反馈连接来提高重建超分辨率图片效果的网络模型 +``` +- 参考论文： + + https://arxiv.org/abs/1903.09814v2 + +- 参考实现： + + https://github.com/turboLIU/SRFBN-tensorflow/blob/master/train.py + +## 默认配置 + +- 训练数据集预处理： + + - 图像的输入尺寸为64*64 +- 测试数据集预处理： + + - 图像的输入尺寸为64*64 +- 训练超参 + + - Batch size： 1 + - Train epoch: 1000 + + +

快速上手

+ +- 数据集准备 +1. 模型训练使用DIV2K数据集。 + +## 模型训练 + +- 单卡训练 + + 1. 配置训练参数。 + + 首先在脚本test/train_performance_1p.sh中，配置batch_size、epochs、data_path等参数，请用户根据实际路径配置data_path，或者在启动训练的命令行中以参数形式下发。 + + ``` + batch_size=1 + epochs=1000 + data_path="../DIV2K/DIV2K_train_HR" + ``` + + 2. 启动训练。 + + 启动单卡训练 （脚本为SRFBN_for_TensorFlow/test/train_performance_1p.sh） + + ``` + bash train_performance_1p.sh --data_path=../DIV2K/DIV2K_train_HR + ``` + +

训练结果

+ +- 精度结果比对 + +| 精度指标项 | GPU实测 | NPU实测 | +| ---------- | ----------- | ----------- | +| PSNR | 6.706763287 | 5.831956861 | + +- 性能结果比对 + +| 性能指标项 | GPU实测 | NPU实测 | +| ---------- | -------------- | -------------- | +| FPS | 3.358950029841 | 4.976489075014 | + + +

高级参考

+ +## 脚本和示例代码 + +``` +├── Basic_Model.py //基本模型代码 +├── README.md //代码说明文档 +├── config.py //模型配置代码 +├── PreProcess.py //数据预处理代码 +├── psnr_ssim.py //图像质量评估代码 +├── requirements.txt //训练python依赖列表 +├── SRFBN_model.py //SRFBN网络模型代码 +├── test.py //测试代码 +├── traditional_blur.py //图像模糊处理代码 +├── train.py //训练代码 +├── test +│ ├──train_performance_1p.sh //单卡训练验证性能启动脚本 + +``` + +## 脚本参数 + +``` +--data_path 数据集路径，默认：path/data +--batch_size 每个NPU的batch size，默认：1 +--epochs 训练epcoh数量，默认：1000 +``` + +## 训练过程 + +1. 通过“模型训练”中的训练指令启动单卡训练。 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py new file mode 100644 index 000000000..a3e01bac3 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py @@ -0,0 +1,181 @@ +from npu_bridge.npu_init import * +import tensorflow as tf +import os +from Basic_Model import basic_network + + +class SRFBN(basic_network): + def __init__(self, sess, cfg): + super(SRFBN, self).__init__(cfg) + self.sess = sess + imageshape = [cfg.batchsize, cfg.imagesize, cfg.imagesize, cfg.c_dim] + labelshape = [cfg.batchsize, cfg.imagesize * cfg.scale, cfg.imagesize * cfg.scale, cfg.c_dim] + self.imageplaceholder = tf.placeholder(dtype=tf.float32, shape=imageshape, name="image") + self.labelplaceholder = tf.placeholder(dtype=tf.float32, shape=labelshape, name="label") + self.last_hidden = None + self.should_reset = True + self.outs = [] + #FB块 + def FeedBackBlock(self, x, num_features, num_groups, act_type, name="FBB"): + if self.cfg.scale == 1: + stride = 1 + padding = "SAME" + kernel_size = 5 + if self.cfg.scale==2: + stride = 2 + padding = "SAME" + kernel_size = 6 + if self.cfg.scale == 3: + stride = 3 + padding = "SAME" + kernel_size = 7 + if self.cfg.scale == 4: + stride = 4 + padding = "SAME" + kernel_size = 8 + if self.should_reset: + self.last_hidden = x + self.should_reset = False + x = tf.concat([x, self.last_hidden], 3) + x = self.ConvBlock(x, 2*num_features, num_features, kernel_size=1, name="FeedBack_compress_in", + act_type=act_type) + + lr_features = [] + hr_features = [] + lr_features.append(x) + for i in range(num_groups): + x = tf.concat(lr_features, 3) + if i > 0: + x = self.ConvBlock(x, num_features*(i+1), num_features, kernel_size=1,stride=1, + padding=padding, act_type=act_type, name="%s_%d"%(name, i)) + x = self.DeConvBlock(x, num_features, num_features, kernel_size=kernel_size, stride=stride, + padding=padding, act_type=act_type, name="%s_%d"%(name, i)) + hr_features.append(x) + x = tf.concat(hr_features, 3) + if i > 0: + x = self.ConvBlock(x, num_features*(i+1), num_features, kernel_size=1, stride=1, + padding=padding, act_type=act_type, name="%s_%d"%(name, i)) + x = self.ConvBlock(x, num_features, num_features, kernel_size=kernel_size, stride=stride, + padding=padding, act_type=act_type, name="%s_%d"%(name, i)) + lr_features.append(x) + del hr_features + + x = tf.concat(lr_features[1:], 3) + + x = self.ConvBlock(x, num_features*num_groups, num_features, kernel_size=1, + act_type=act_type, name="FeedBack_compress_out") + + self.last_hidden = x + + return x + + def build(self): + if self.cfg.scale == 2: + stride = 2 + padding = "SAME" + kernel_size = 6 + if self.cfg.scale == 3: + stride = 3 + padding = "SAME" + kernel_size = 7 + if self.cfg.scale == 4: + stride = 4 + padding = "SAME" + kernel_size = 8 + # x = self.sub_mean(self.imageplaceholder) # 暂且当作归一化 + + _, height, width, _ = self.imageplaceholder.get_shape().as_list() + + inter_size = tf.constant([height*self.cfg.scale, width*self.cfg.scale]) + inter_res = tf.image.resize_images(self.imageplaceholder, inter_size) + # inter_res = self.imageplaceholder + + x = self.ConvBlock(self.imageplaceholder, self.cfg.in_channels, 4 * self.cfg.num_features, kernel_size=3, + act_type=self.cfg.act_type, padding="SAME", name="conv_in") + x = self.ConvBlock(x, 4*self.cfg.num_features, self.cfg.num_features, kernel_size=1, + act_type=self.cfg.act_type, padding="SAME", name="feat_in") + # outs = [] + for i in range(self.cfg.num_steps): + if i == 0: + self.should_reset=True + t = self.FeedBackBlock(x, self.cfg.num_features, self.cfg.num_groups, self.cfg.act_type, name="FBB_%d"%i) + t = self.DeConvBlock(t, self.cfg.num_features, self.cfg.num_features, kernel_size=kernel_size, + stride=stride, padding=padding, act_type="relu", name="out_%d"%i) + t = self.ConvBlock(t, self.cfg.num_features, self.cfg.out_channels, kernel_size=3, stride=1, + act_type="tanh", padding="SAME", name="conv_out") + t = inter_res + t + t = tf.clip_by_value(t, -1.0, 1.0) + # t = t + inter_res + # t = self.add_mean(t) + self.outs.append(t) + #训练步骤 + def train_step(self): + self.build() + print("This Net has Params num is %f MB" % (self.params_count * 4 / 1024 / 1024)) # float32 + tf.summary.image("image/HR", self.labelplaceholder, max_outputs=1) + out = tf.add_n(self.outs)/self.cfg.num_steps + + tf.summary.image("image/SR", out, max_outputs=1) + tf.summary.image("image/LR", self.imageplaceholder, max_outputs=1) + + self.l2_regularization_loss = tf.reduce_sum(tf.get_collection("weights_l2_loss")) + + self.losses = [self.calc_loss(x=x, y=self.labelplaceholder, loss_type=self.cfg.loss_type) for x in self.outs] + self.losses = tf.reduce_sum(self.losses)/len(self.losses)/self.cfg.batchsize + self.l2_regularization_loss + + tf.summary.scalar('loss/total', self.losses) + tf.summary.scalar('loss/l2_loss', self.l2_regularization_loss) + + self.merged_summary = tf.summary.merge_all() + self.saver = tf.train.Saver(max_to_keep=1) + #加载检查点 + def load(self): + model_name = "SRFBN.model" + model_dir = "%s_%s_%s_%s_c%d_x%s" % ( + "SRFBN", self.cfg.num_features, self.cfg.num_steps, self.cfg.num_groups, self.cfg.c_dim, self.cfg.scale) + checkpoint_dir = os.path.join(self.cfg.checkpoint_dir, model_dir) + ckpt = tf.train.get_checkpoint_state(checkpoint_dir) + if ckpt and ckpt.model_checkpoint_path: + ckpt_path = str(ckpt.model_checkpoint_path) + self.saver.restore(self.sess, os.path.join(os.getcwd(), ckpt_path)) + step = int(os.path.basename(ckpt_path).split('-')[1]) + print("\nCheckpoint Loading Success! %s\n" % ckpt_path) + else: + step = 0 + print("\nCheckpoint Loading Failed! \n") + + return step + #保存当前模型 + def save(self, step): + model_name = "SRFBN.model" + model_dir = "%s_%s_%s_%s_c%d_x%s" % \ + ("SRFBN", self.cfg.num_features, self.cfg.num_steps, + self.cfg.num_groups, self.cfg.c_dim, self.cfg.scale) + checkpoint_dir = os.path.join(self.cfg.checkpoint_dir, model_dir) + + if not os.path.exists(checkpoint_dir): + os.makedirs(checkpoint_dir) + + self.saver.save(self.sess, + os.path.join(checkpoint_dir, model_name), + global_step=step) + #测试 + def test(self, width, height): + self.cfg.batchsize = 1 + testshape = [self.cfg.batchsize, height, width, self.cfg.c_dim] + labelshape = [self.cfg.batchsize, height*self.cfg.scale, width*self.cfg.scale, self.cfg.c_dim] + self.imageplaceholder = tf.placeholder(dtype=tf.float32, shape=testshape) + self.labelplaceholder = tf.placeholder(dtype=tf.float32, shape=labelshape) + self.build() + # self.outs = [self.add_mean(x) for x in self.outs] + out = tf.add_n(self.outs)/self.cfg.num_steps + # out = tf.concat(self.outs, -1) + return out + + +if __name__ == '__main__': + from config import SRFBN_config as config + cfg = config() + sess = tf.Session(config=npu_config_proto()) + net = SRFBN(sess, cfg) + train_step = net.train_step() diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py new file mode 100644 index 000000000..d6d3b5592 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py @@ -0,0 +1,53 @@ +from npu_bridge.npu_init import * +import os + +class config: + def __init__(self): + self.batchsize = 1#一次处理的样本数量 + self.Process_num = 3 #进程数量 + self.maxsize = 200 #最大的大小 + self.ngpu = 1 #gpu数量 + self.imagesize = 64#图片大小 + self.scale = 3#缩放规模 + self.epoch = 1000#迭代次数 + #创建检查点，记录，结果目录 + self.checkpoint_dir = "./model"#检查点目录 + if not os.path.exists(self.checkpoint_dir): + os.mkdir(self.checkpoint_dir) + self.log_dir = "./log" + if not os.path.exists(self.log_dir): + os.mkdir(self.log_dir) + self.result = "./result" + if not os.path.exists(self.result): + os.mkdir(self.result) + + + +class SRFBN_config(config): + def __init__(self): + super(SRFBN_config, self).__init__() + self.istrain = True#正在训练还是正在测试 + self.istest = not self.istrain + self.c_dim = 3 #color channel 可以训练灰度图也可以训练RGB图 + self.in_channels = 3 + self.out_channels = 3 + self.num_features = 32#base number of filter + self.num_steps = 4#时间步 + self.num_groups = 6#FBB中feedbackblock中projection group数量 + self.BN = True# + if self.BN: + self.BN_type = "BN" # "BN" # or "IN" + self.act_type = "prelu" #activation function + self.loss_type = "L2" + self.lr_steps = [150, 300, 550, 750]#迭代次数表 + self.lr_gama = 1#参数 + self.learning_rate = 2e-7#学习率 + self.load_premodel = True + #创建目录 + self.srfbn_logdir = "%s/srfbn" % self.log_dir + if not os.path.exists(self.srfbn_logdir): + os.mkdir(self.srfbn_logdir) + self.srfbn_result = "%s/srfbn" % self.result + if not os.path.exists(self.srfbn_result): + os.mkdir(self.srfbn_result) + diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py new file mode 100644 index 000000000..c27924230 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py @@ -0,0 +1,86 @@ +import numpy as np +import cv2 + +#灰度图psnr +def compare_psnr_gray(im1,im2,maxI=255): + im1=im1.astype(np.float64) + im2=im2.astype(np.float64) + diff=im1-im2 + mse=np.mean(np.square(diff)) + if mse==0: return float('inf') + return 10*np.log10(maxI*maxI/mse) + +#彩色图psnr +def compare_psnr_rgb(im1,im2,maxI=255): + blue1,green1,red1=cv2.split(im1) + blue2,green2,red2=cv2.split(im2) + psnr_blue=compare_psnr_gray(blue1,blue2,maxI) + psnr_green=compare_psnr_gray(green1,green2,maxI) + psnr_red=compare_psnr_gray(red1,red2,maxI) + + #三通道取平均值 + return (psnr_blue+psnr_green+psnr_red)/3 + +#可以直接用的（不能把灰度图和彩色图直接比较） +def compare_psnr(im1,im2,maxI=255): + #如果两张图片大小不同或者同为同一类型（灰度、彩色）图就报错 + if im1.shape!=im2.shape: raise ValueError("错误：图片大小维度不同") + if (im1.ndim==2) and (im2.ndim==2): return compare_psnr_gray(im1,im2) + #彩色图里可能有单通道（？） + elif (im1.ndim==3) and (im2.ndim==3): + if im1.shape[2]==3: + return compare_psnr_rgb(im1,im2) + elif im1.shape[2]==1: + return compare_psnr_gray(np.squeeze(im1),np.squeeze(im2)) + else: raise ValueError("错误：错误的图片维度") + +#ssim +def ssim(im1,im2,maxI=255): + #0.01和0.03是已经定下来的常数，不要改 + c1=(0.01*maxI)**2 + c2=(0.03*maxI)**2 + + #转换成float64类型 + im1=im1.astype(np.float64) + im2=im2.astype(np.float64) + #高斯核，这里11和1.5也是定下来的不要改 + kernel=cv2.getGaussianKernel(11,1.5) + window=np.outer(kernel,kernel.transpose()) + + #求卷积 + #ssim先将图片分成若干个小块后分别按照公式的各元素求各种卷积 + mu1=cv2.filter2D(im1,-1,window)[5:-5,5:-5] + mu2=cv2.filter2D(im2,-1,window)[5:-5,5:-5] + mu1_sq=mu1**2 + mu2_sq=mu2**2 + mu1_mu2=mu1*mu2 + sigma1_sq=cv2.filter2D(im1**2,-1,window)[5:-5,5:-5]-mu1_sq + sigma2_sq=cv2.filter2D(im2**2,-1,window)[5:-5,5:-5]-mu2_sq + sigma12=cv2.filter2D(im1*im2,-1,window)[5:-5,5:-5]-mu1_mu2 + + #ssim的计算公式 + ssim_map=((2*mu1_mu2+c1)*(2*sigma12+c2))/((mu1_sq+mu2_sq+c1)*(sigma1_sq+sigma2_sq+c2)) + #取所有小块计算结果的平均值 + return ssim_map.mean() + +#可以直接用的 +def compare_ssim(im1,im2,maxI=255): + #如果两张图片大小不同或者同为同一类型（灰度、彩色）图就报错 + if im1.shape!=im2.shape: + raise ValueError("错误：图片维度大小不同") + if im1.ndim==2: + return ssim(im1,im2) + #彩色图里可能有单通道（？） + elif im1.ndim==3: + if im1.shape[2]==3: + blue1,green1,red1=cv2.split(im1) + blue2,green2,red2=cv2.split(im2) + ssim_blue=ssim(blue1,blue2) + ssim_green=ssim(green1,green2) + ssim_red=ssim(red1,red2) + + #同psnr，取平均值 + return (ssim_blue+ssim_green+ssim_red)/3 + elif im1.shape[2]==1: + return ssim(np.squeeze(im1),np.squeeze(im2)) + else: raise ValueError("错误：错误的图片维度") \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/requirements.txt b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/requirements.txt new file mode 100644 index 000000000..e04023578 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/requirements.txt @@ -0,0 +1,4 @@ +tensorflow==1.15.0 +cv2 +numpy +scikit-image \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py new file mode 100644 index 000000000..57457c50a --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py @@ -0,0 +1,61 @@ + +from npu_bridge.npu_init import * +import tensorflow as tf + +from SRFBN_model import SRFBN +from PreProcess import * +from skimage.metrics import peak_signal_noise_ratio as compare_psnr +from skimage.metrics import _structural_similarity + + + + +def test_SRFBN(image_lr,image_hr): + + #image + height, width, _ = image_lr.shape + print(height,width) + global load_flag + global srfbn + global out + if load_flag == 0: + srfbn = SRFBN(sess, cfg) + out = srfbn.test(width, height) + tf.global_variables_initializer().run(session=sess) + srfbn.saver = tf.train.Saver(max_to_keep=1) + srfbn.load() + srfbn.l2_regularization_loss = tf.reduce_sum(tf.get_collection("weights_l2_loss")) + srfbn.losses = [srfbn.calc_loss(x=x, y=srfbn.labelplaceholder, loss_type=srfbn.cfg.loss_type) for x in + srfbn.outs] + srfbn.losses = tf.reduce_sum(srfbn.losses) / len(srfbn.losses) / srfbn.cfg.batchsize + srfbn.l2_regularization_loss + load_flag += 1 + cv2.namedWindow("result", 0) + + img_hr = image_hr.reshape([1,height*srfbn.cfg.scale,width*srfbn.cfg.scale,3]) + img_lr = image_lr.reshape([1, height, width, 3]) + output,err,l2_loss = sess.run([out,srfbn.losses,srfbn.l2_regularization_loss], feed_dict={srfbn.imageplaceholder: img_lr,srfbn.labelplaceholder:img_hr}) + output = output[0] * 128 + 127.5 + img_hr = img_hr.reshape([height*srfbn.cfg.scale,width*srfbn.cfg.scale,3]) + PSNR = compare_psnr(output, img_hr, data_range=255) + ssim = _structural_similarity.structural_similarity(output, img_hr,win_size=11, data_range=255, multichannel=True) + print("loss:[%.8f], l2_loss:[%.8f], PSNR:[%.8f], SSIM:[%.8f]"%(err,l2_loss,PSNR,ssim)) + cv2.imshow("result", np.uint8(output)) + cv2.waitKey(0) + + + +if __name__ == '__main__': + sess = tf.Session(config=npu_config_proto()) + from config import SRFBN_config + cfg = SRFBN_config() + cfg.istest = True + cfg.istrain = False + image = "/home/TestUser08/BUAA/Resolution_2K/DIV2K/DIV2K_valid_HR/0801.png" + batch_label,batch_lrimage = preprocess([image,],cfg) + batch_lrimage = np.array(batch_lrimage) + batch_label = np.array(batch_label) + load_flag = 0 + for i in range(batch_label.shape[0]): + test_SRFBN(batch_lrimage[i],batch_label[i]) + srfbn.sess.close() + diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py new file mode 100644 index 000000000..0d83abd5e --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py @@ -0,0 +1,508 @@ +from npu_bridge.npu_init import * +import numpy as np +import scipy, cv2 +from scipy import fftpack + + +''' +modified by Kai Zhang (github: https://github.com/cszn) +03/03/2019 +''' + + +# -------------------------------- +# get rho and sigma +# -------------------------------- +def get_rho_sigma(sigma=2.55/255, iter_num=15): + ''' + Kai Zhang (github: https://github.com/cszn) + 03/03/2019 + ''' + modelSigma1 = 49.0 + modelSigma2 = 2.55 + modelSigmaS = np.logspace(np.log10(modelSigma1), np.log10(modelSigma2), iter_num) + sigmas = modelSigmaS/255. + mus = list(map(lambda x: (sigma**2)/(x**2)/3, sigmas)) + rhos = mus + return rhos, sigmas + + +# -------------------------------- +# HWC, get uperleft and denominator +# -------------------------------- +def get_uperleft_denominator(img, kernel): + ''' + Kai Zhang (github: https://github.com/cszn) + 03/03/2019 + ''' + V = psf2otf(kernel, img.shape[:2]) # discrete fourier transform of kernel + denominator = np.expand_dims(np.abs(V)**2, axis=2) # Fourier transform of K transpose * K + upperleft = np.expand_dims(np.conj(V), axis=2) * np.fft.fft2(img, axes=[0, 1]) + return upperleft, denominator + + +# otf2psf: not sure where I got this one from. Maybe translated from Octave source code or whatever. It's just math. +def otf2psf(otf, outsize=None): + insize = np.array(otf.shape) + psf = np.fft.ifftn(otf, axes=(0, 1)) + for axis, axis_size in enumerate(insize): + psf = np.roll(psf, np.floor(axis_size / 2).astype(int), axis=axis) + if type(outsize) != type(None): + insize = np.array(otf.shape) + outsize = np.array(outsize) + n = max(np.size(outsize), np.size(insize)) + # outsize = postpad(outsize(:), n, 1); + # insize = postpad(insize(:) , n, 1); + colvec_out = outsize.flatten().reshape((np.size(outsize), 1)) + colvec_in = insize.flatten().reshape((np.size(insize), 1)) + outsize = np.pad(colvec_out, ((0, max(0, n - np.size(colvec_out))), (0, 0)), mode="constant") + insize = np.pad(colvec_in, ((0, max(0, n - np.size(colvec_in))), (0, 0)), mode="constant") + + pad = (insize - outsize) / 2 + if np.any(pad < 0): + print("otf2psf error: OUTSIZE must be smaller than or equal than OTF size") + prepad = np.floor(pad) + postpad = np.ceil(pad) + dims_start = prepad.astype(int) + dims_end = (insize - postpad).astype(int) + for i in range(len(dims_start.shape)): + psf = np.take(psf, range(dims_start[i][0], dims_end[i][0]), axis=i) + n_ops = np.sum(otf.size * np.log2(otf.shape)) + psf = np.real_if_close(psf, tol=n_ops) + return psf + + +# psf2otf copied/modified from https://github.com/aboucaud/pypher/blob/master/pypher/pypher.py +def psf2otf(psf, shape=None): + """ + Convert point-spread function to optical transfer function. + Compute the Fast Fourier Transform (FFT) of the point-spread + function (PSF) array and creates the optical transfer function (OTF) + array that is not influenced by the PSF off-centering. + By default, the OTF array is the same size as the PSF array. + To ensure that the OTF is not altered due to PSF off-centering, PSF2OTF + post-pads the PSF array (down or to the right) with zeros to match + dimensions specified in OUTSIZE, then circularly shifts the values of + the PSF array up (or to the left) until the central pixel reaches (1,1) + position. + Parameters + ---------- + psf : `numpy.ndarray` + PSF array + shape : int + Output shape of the OTF array + Returns + ------- + otf : `numpy.ndarray` + OTF array + Notes + ----- + Adapted from MATLAB psf2otf function + """ + if type(shape) == type(None): + shape = psf.shape + shape = np.array(shape) + if np.all(psf == 0): + # return np.zeros_like(psf) + return np.zeros(shape) + if len(psf.shape) == 1: + psf = psf.reshape((1, psf.shape[0])) + inshape = psf.shape + psf = zero_pad(psf, shape, position='corner') + for axis, axis_size in enumerate(inshape): + psf = np.roll(psf, -int(axis_size / 2), axis=axis) + # Compute the OTF + otf = np.fft.fft2(psf, axes=(0, 1)) + # Estimate the rough number of operations involved in the FFT + # and discard the PSF imaginary part if within roundoff error + # roundoff error = machine epsilon = sys.float_info.epsilon + # or np.finfo().eps + n_ops = np.sum(psf.size * np.log2(psf.shape)) + otf = np.real_if_close(otf, tol=n_ops) + return otf + + +def zero_pad(image, shape, position='corner'): + """ + Extends image to a certain size with zeros + Parameters + ---------- + image: real 2d `numpy.ndarray` + Input image + shape: tuple of int + Desired output shape of the image + position : str, optional + The position of the input image in the output one: + * 'corner' + top-left corner (default) + * 'center' + centered + Returns + ------- + padded_img: real `numpy.ndarray` + The zero-padded image + """ + shape = np.asarray(shape, dtype=int) + imshape = np.asarray(image.shape, dtype=int) + if np.alltrue(imshape == shape): + return image + if np.any(shape <= 0): + raise ValueError("ZERO_PAD: null or negative shape given") + dshape = shape - imshape + if np.any(dshape < 0): + raise ValueError("ZERO_PAD: target size smaller than source one") + pad_img = np.zeros(shape, dtype=image.dtype) + idx, idy = np.indices(imshape) + if position == 'center': + if np.any(dshape % 2 != 0): + raise ValueError("ZERO_PAD: source and target shapes " + "have different parity.") + offx, offy = dshape // 2 + else: + offx, offy = (0, 0) + pad_img[idx + offx, idy + offy] = image + return pad_img + + +''' +Reducing boundary artifacts +''' + + +def opt_fft_size(n): + ''' + Kai Zhang (github: https://github.com/cszn) + 03/03/2019 + # opt_fft_size.m + # compute an optimal data length for Fourier transforms + # written by Sunghyun Cho (sodomau@postech.ac.kr) + # persistent opt_fft_size_LUT; + ''' + + LUT_size = 2048 + # print("generate opt_fft_size_LUT") + opt_fft_size_LUT = np.zeros(LUT_size) + + e2 = 1 + while e2 <= LUT_size: + e3 = e2 + while e3 <= LUT_size: + e5 = e3 + while e5 <= LUT_size: + e7 = e5 + while e7 <= LUT_size: + if e7 <= LUT_size: + opt_fft_size_LUT[e7-1] = e7 + if e7*11 <= LUT_size: + opt_fft_size_LUT[e7*11-1] = e7*11 + if e7*13 <= LUT_size: + opt_fft_size_LUT[e7*13-1] = e7*13 + e7 = e7 * 7 + e5 = e5 * 5 + e3 = e3 * 3 + e2 = e2 * 2 + + nn = 0 + for i in range(LUT_size, 0, -1): + if opt_fft_size_LUT[i-1] != 0: + nn = i-1 + else: + opt_fft_size_LUT[i-1] = nn+1 + + m = np.zeros(len(n)) + for c in range(len(n)): + nn = n[c] + if nn <= LUT_size: + m[c] = opt_fft_size_LUT[nn-1] + else: + m[c] = -1 + return m + + +def wrap_boundary_liu(img, img_size): + + """ + Reducing boundary artifacts in image deconvolution + Renting Liu, Jiaya Jia + ICIP 2008 + """ + if img.ndim == 2: + ret = wrap_boundary(img, img_size) + elif img.ndim == 3: + ret = [wrap_boundary(img[:, :, i], img_size) for i in range(3)] + ret = np.stack(ret, 2) + return ret + + +def wrap_boundary(img, img_size): + + """ + python code from: + https://github.com/ys-koshelev/nla_deblur/blob/90fe0ab98c26c791dcbdf231fe6f938fca80e2a0/boundaries.py + Reducing boundary artifacts in image deconvolution + Renting Liu, Jiaya Jia + ICIP 2008 + """ + (H, W) = np.shape(img) + H_w = int(img_size[0]) - H + W_w = int(img_size[1]) - W + + # ret = np.zeros((img_size[0], img_size[1])); + alpha = 1 + HG = img[:, :] + + r_A = np.zeros((alpha*2+H_w, W)) + r_A[:alpha, :] = HG[-alpha:, :] + r_A[-alpha:, :] = HG[:alpha, :] + a = np.arange(H_w)/(H_w-1) + # r_A(alpha+1:end-alpha, 1) = (1-a)*r_A(alpha,1) + a*r_A(end-alpha+1,1) + r_A[alpha:-alpha, 0] = (1-a)*r_A[alpha-1, 0] + a*r_A[-alpha, 0] + # r_A(alpha+1:end-alpha, end) = (1-a)*r_A(alpha,end) + a*r_A(end-alpha+1,end) + r_A[alpha:-alpha, -1] = (1-a)*r_A[alpha-1, -1] + a*r_A[-alpha, -1] + + r_B = np.zeros((H, alpha*2+W_w)) + r_B[:, :alpha] = HG[:, -alpha:] + r_B[:, -alpha:] = HG[:, :alpha] + a = np.arange(W_w)/(W_w-1) + r_B[0, alpha:-alpha] = (1-a)*r_B[0, alpha-1] + a*r_B[0, -alpha] + r_B[-1, alpha:-alpha] = (1-a)*r_B[-1, alpha-1] + a*r_B[-1, -alpha] + + if alpha == 1: + A2 = solve_min_laplacian(r_A[alpha-1:, :]) + B2 = solve_min_laplacian(r_B[:, alpha-1:]) + r_A[alpha-1:, :] = A2 + r_B[:, alpha-1:] = B2 + else: + A2 = solve_min_laplacian(r_A[alpha-1:-alpha+1, :]) + r_A[alpha-1:-alpha+1, :] = A2 + B2 = solve_min_laplacian(r_B[:, alpha-1:-alpha+1]) + r_B[:, alpha-1:-alpha+1] = B2 + A = r_A + B = r_B + + r_C = np.zeros((alpha*2+H_w, alpha*2+W_w)) + r_C[:alpha, :] = B[-alpha:, :] + r_C[-alpha:, :] = B[:alpha, :] + r_C[:, :alpha] = A[:, -alpha:] + r_C[:, -alpha:] = A[:, :alpha] + + if alpha == 1: + C2 = C2 = solve_min_laplacian(r_C[alpha-1:, alpha-1:]) + r_C[alpha-1:, alpha-1:] = C2 + else: + C2 = solve_min_laplacian(r_C[alpha-1:-alpha+1, alpha-1:-alpha+1]) + r_C[alpha-1:-alpha+1, alpha-1:-alpha+1] = C2 + C = r_C + # return C + A = A[alpha-1:-alpha-1, :] + B = B[:, alpha:-alpha] + C = C[alpha:-alpha, alpha:-alpha] + ret = np.vstack((np.hstack((img, B)), np.hstack((A, C)))) + return ret + + +def solve_min_laplacian(boundary_image): + (H, W) = np.shape(boundary_image) + + # Laplacian + f = np.zeros((H, W)) + # boundary image contains image intensities at boundaries + boundary_image[1:-1, 1:-1] = 0 + j = np.arange(2, H)-1 + k = np.arange(2, W)-1 + f_bp = np.zeros((H, W)) + f_bp[np.ix_(j, k)] = -4*boundary_image[np.ix_(j, k)] + boundary_image[np.ix_(j, k+1)] + boundary_image[np.ix_(j, k-1)] + boundary_image[np.ix_(j-1, k)] + boundary_image[np.ix_(j+1, k)] + + del(j, k) + f1 = f - f_bp # subtract boundary points contribution + del(f_bp, f) + + # DST Sine Transform algo starts here + f2 = f1[1:-1,1:-1] + del(f1) + + # compute sine tranform + if f2.shape[1] == 1: + tt = fftpack.dst(f2, type=1, axis=0)/2 + else: + tt = fftpack.dst(f2, type=1)/2 + + if tt.shape[0] == 1: + f2sin = np.transpose(fftpack.dst(np.transpose(tt), type=1, axis=0)/2) + else: + f2sin = np.transpose(fftpack.dst(np.transpose(tt), type=1)/2) + del(f2) + + # compute Eigen Values + [x, y] = np.meshgrid(np.arange(1, W-1), np.arange(1, H-1)) + denom = (2*np.cos(np.pi*x/(W-1))-2) + (2*np.cos(np.pi*y/(H-1)) - 2) + + # divide + f3 = f2sin/denom + del(f2sin, x, y) + + # compute Inverse Sine Transform + if f3.shape[0] == 1: + tt = fftpack.idst(f3*2, type=1, axis=1)/(2*(f3.shape[1]+1)) + else: + tt = fftpack.idst(f3*2, type=1, axis=0)/(2*(f3.shape[0]+1)) + del(f3) + if tt.shape[1] == 1: + img_tt = np.transpose(fftpack.idst(np.transpose(tt)*2, type=1)/(2*(tt.shape[0]+1))) + else: + img_tt = np.transpose(fftpack.idst(np.transpose(tt)*2, type=1, axis=0)/(2*(tt.shape[1]+1))) + del(tt) + + # put solution in inner points; outer points obtained from boundary image + img_direct = boundary_image + img_direct[1:-1, 1:-1] = 0 + img_direct[1:-1, 1:-1] = img_tt + return img_direct + + +""" +Created on Thu Jan 18 15:36:32 2018 +@author: italo +https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py +""" + +""" +Syntax +h = fspecial(type) +h = fspecial('average',hsize) +h = fspecial('disk',radius) +h = fspecial('gaussian',hsize,sigma) +h = fspecial('laplacian',alpha) +h = fspecial('log',hsize,sigma) +h = fspecial('motion',len,theta) +h = fspecial('prewitt') +h = fspecial('sobel') +""" + + +def fspecial_average(hsize=3): + """Smoothing filter""" + return np.ones((hsize, hsize))/hsize**2 + + +def fspecial_disk(radius): + """Disk filter""" + # raise(NotImplemented) + # rad = 0.6 + rad = radius + crad = np.ceil(rad-0.5) + [x, y] = np.meshgrid(np.arange(-crad, crad+1), np.arange(-crad, crad+1)) + maxxy = np.zeros(x.shape) + maxxy[abs(x) >= abs(y)] = abs(x)[abs(x) >= abs(y)] + maxxy[abs(y) >= abs(x)] = abs(y)[abs(y) >= abs(x)] + minxy = np.zeros(x.shape) + minxy[abs(x) <= abs(y)] = abs(x)[abs(x) <= abs(y)] + minxy[abs(y) <= abs(x)] = abs(y)[abs(y) <= abs(x)] + m1 = (rad**2 < (maxxy+0.5)**2 + (minxy-0.5)**2)*(minxy-0.5) +\ + (rad**2 >= (maxxy+0.5)**2 + (minxy-0.5)**2)*\ + np.sqrt((rad**2 + 0j) - (maxxy + 0.5)**2) + m2 = (rad**2 > (maxxy-0.5)**2 + (minxy+0.5)**2)*(minxy+0.5) +\ + (rad**2 <= (maxxy-0.5)**2 + (minxy+0.5)**2)*\ + np.sqrt((rad**2 + 0j) - (maxxy - 0.5)**2) + # sgrid = (rad**2 * (0.5*(asin(m2/rad) - asin(m1/rad)) + 0.25*(sin(2*asin(m2/rad)) - ) + h = None + return h + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0]-1.0)/2.0, (hsize[1]-1.0)/2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1]+1), np.arange(-siz[0], siz[0]+1)) + arg = -(x*x + y*y)/(2*std*std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h/sumh + return h + + + +def fspecial(filter_type, *args, **kwargs): + ''' + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + ''' + if filter_type == 'average': + return fspecial_average(*args, **kwargs) + if filter_type == 'disk': + return fspecial_disk(*args, **kwargs) + if filter_type == 'gaussian': + return fspecial_gaussian(*args, **kwargs) + + +def traditional_blur(img, filter_type): + ksize = np.random.choice([3, 5], 1) + # ksize = 3 + if filter_type == 'average': + img = np.array(img, np.float32) + blur_img = cv2.medianBlur(img, ksize=ksize) + return blur_img + if filter_type == 'gaussian': + blur_img = cv2.GaussianBlur(img, ksize=(ksize, ksize), sigmaX=0, sigmaY=0) + return blur_img + +def motion_blur(img, angle=45): + degree = np.random.randint(1, 8, 1) # 8模糊度已经足够，轻重模糊随机 + image = np.array(img) + + # 这里生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高 + M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1) + motion_blur_kernel = np.diag(np.ones(degree)) + motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree)) + + motion_blur_kernel = motion_blur_kernel / degree + blurred = cv2.filter2D(image, -1, motion_blur_kernel) + + # convert to uint8 + cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX) + blurred = (blurred-127.5)/128 + return blurred + + +if __name__ == '__main__': + a = opt_fft_size([111]) + print(a) + + print(fspecial('gaussian', 5, 1)) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py new file mode 100644 index 000000000..58a4e6660 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py @@ -0,0 +1,124 @@ + + + +from npu_bridge.npu_init import * +from SRFBN_model import SRFBN +import tensorflow as tf +import time +from PreProcess import * +from skimage.metrics import peak_signal_noise_ratio as comparepsnr +from skimage.metrics import _structural_similarity + +def train_SRFBN(dataset, sess, cfg): + # start put data in queue + with tf.device('/cpu:0'): + step = tf.Variable(0, trainable=False) + srfbn = SRFBN(sess=sess, cfg=cfg) + srfbn.train_step() + out = tf.add_n(srfbn.outs) / srfbn.cfg.num_steps + ## build Optimizer + #使学习率在不同迭代阶段不同 + boundaries = [len(dataset)*epoch//cfg.batchsize for epoch in cfg.lr_steps] + values = [cfg.learning_rate*(cfg.lr_gama**i) for i in range(len(cfg.lr_steps)+1)] + lr = tf.train.piecewise_constant(step, boundaries, values) + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + optimizer = tf.train.AdamOptimizer(learning_rate=lr) + with tf.control_dependencies(update_ops): + gs_vs = optimizer.compute_gradients(srfbn.losses) + with tf.device('/cpu:0'): + train_op = optimizer.apply_gradients(grads_and_vars=gs_vs, global_step=step) + + tf.global_variables_initializer().run(session=sess) + + summary_writer = tf.summary.FileWriter(cfg.srfbn_logdir, srfbn.sess.graph) + #加载模型 + if srfbn.cfg.load_premodel: + counter = srfbn.load() + else: + counter = 0 + time_ = time.time() + print("\nNow Start Training...\n") + global_step = 0 + for ep in range(cfg.epoch): + + #每次训练时挑选照片的顺序是随机的 + pic_idx = np.random.permutation(len(dataset)) + picid = 0 + #一次加载五张图像 + for i in range(0,len(dataset),5): + index = [] + for j in range(5): + index.append(pic_idx[i+j]) + imgnames = [] + for pic in index: + imgnames.append(dataset[pic]) + picid += 5 + print(imgnames) + batch_labels, batch_images = preprocess(imgnames, cfg) + patch_idx = list(range(len(batch_labels))) + #使得图片块的数量刚好能被batchsize整除 + if len(patch_idx) % cfg.batchsize != 0: + patch_idx.extend(list(np.random.choice(patch_idx, + cfg.batchsize * ((len(patch_idx) // cfg.batchsize)+1) - len(patch_idx)))) + + + patch_idx = np.random.permutation(patch_idx) + + + iterations = len(patch_idx) // cfg.batchsize + + + for it in range(iterations): + + idx = list(patch_idx[it * cfg.batchsize: (it+1)* cfg.batchsize]) + + + patch_labels = np.array(batch_labels)[idx] + + patch_images = np.array(batch_images)[idx] + + + output,_, loss,l2_loss,= srfbn.sess.run([out,train_op, srfbn.losses,srfbn.l2_regularization_loss], + feed_dict={srfbn.imageplaceholder: patch_images, + srfbn.labelplaceholder: patch_labels}) + output = output[0] * 128 + 127.5 + img_hr = patch_labels.reshape([srfbn.cfg.imagesize * srfbn.cfg.scale, srfbn.cfg.imagesize * srfbn.cfg.scale, 3]) + PSNR = comparepsnr(output, img_hr, data_range=255) + ssim = _structural_similarity.structural_similarity(output, img_hr, win_size=11, data_range=255, + multichannel=True) + + if it % 10 == 0: + print("Epoch:%2d, pic:%d, step:%2d, global_step:%d, time :%4.4f, loss:%.8f, l2_loss:%.8f, PSNR:%.8f, SSIM:%.8f" % ( + (ep + 1),picid, it,global_step,time.time() - time_, loss,l2_loss,PSNR,ssim)) + if it % 100 == 0: + srfbn.save(counter) + summary_str = srfbn.sess.run(srfbn.merged_summary, + feed_dict={srfbn.imageplaceholder: patch_images, + srfbn.labelplaceholder: patch_labels}) + summary_writer.add_summary(summary_str, counter) + + global_step += 1 + counter += 1 + +#训练 +def train(*args, **kwargs): + data_dir = kwargs["data_dir"] + imgs = [os.path.join(data_dir,data) for data in os.listdir(data_dir)] + + + + sess = tf.compat.v1.Session(config=npu_config_proto()) + + ## build NetWork + from config import SRFBN_config + cfg = SRFBN_config() + datasetet = imgs + train_SRFBN(datasetet, sess, cfg) + + + +if __name__ == '__main__': + import os + data_dir = "/home/TestUser08/BUAA/output_npu_20221021153629/SRFBN-tensorflow_npu_20221021153629/Resolution_2K/DIV2K/DIV2K_train_HR" + train(data_dir=data_dir) + -- Gitee From c96ebeeb97ea4f6dd71e8833c12eaf19bedd0191 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 03:12:02 +0000 Subject: [PATCH 03/26] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20test?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/.keep diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/.keep b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From de091f72fa9f8f07fe9b5284de8d6772d931c2ca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 03:12:25 +0000 Subject: [PATCH 04/26] =?UTF-8?q?=E6=80=A7=E8=83=BD=E8=AE=AD=E7=BB=83?= =?UTF-8?q?=E8=84=9A=E6=9C=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../test/train_performance_1p.sh | 153 ++++++++++++++++++ 1 file changed, 153 insertions(+) create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh new file mode 100644 index 000000000..5882b5f4f --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh @@ -0,0 +1,153 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo "usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./output/${ASCEND_DEVICE_ID}" +fi + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 90行，请一定不要、不要、不要修改########## +#########第3行 至 90行，请一定不要、不要、不要修改########## +#########第3行 至 90行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +train_epochs=1 +batch_size=1 +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +python3 ./train.py > ${print_log} +# 性能相关数据计算 +StepTime=`((cat ${print_log} | grep "time" | head -n 1) && (cat ${print_log} | grep "time" | tail -n 1)) | awk -F ':' '{print $5 $6 }' | awk -F ',' '{print $1 $2}' | awk -F ' ' '{print $1;print $3}' | awk '{if (NR == 1){a=$1} else if (NR == 2){b=$1} else if (NR == 3){c=$1} else if (NR == 4){d=$1}} END {print (d-b)/(c-a)}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` +#PSNR值计算 +PSNR=`cat ${print_log} | grep "time" | tail -n 10 | awk -F ',' '{print $8}' | awk -F ':' '{sum+=$2} END {print sum/NR}'` +# 提取所有loss打印信息 +grep "loss:" ${print_log} | awk -F "," '{print $6}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" +echo "PSNR : $PSNR" +# 输出训练精度 +#echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -- Gitee From 4e08e4d21a69d974fcfd445d821e7b7a66f2191d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:02:51 +0000 Subject: [PATCH 05/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/Basic_Model.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py index e902cf5b6..3051156e8 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py @@ -1,3 +1,31 @@ +# 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.contrib import layers -- Gitee From 2ae87e9e290ed24113405e05786a48604ac29972 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:03:30 +0000 Subject: [PATCH 06/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/PreProcess.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py index c38999b3c..0d13c7cac 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py @@ -1,3 +1,31 @@ +# 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 cv2 import numpy as np -- Gitee From ca798014da0ed3949eec721d1bd738d82c2395ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:03:54 +0000 Subject: [PATCH 07/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/SRFBN_model.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py index a3e01bac3..1ce90c923 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py @@ -1,3 +1,31 @@ +# 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 os -- Gitee From b5171ef392a47d59f534ad685709804bd5c4c970 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:04:16 +0000 Subject: [PATCH 08/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../contrib/cv/SRFBN_for_TensorFlow/config.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py index d6d3b5592..4da2aec8f 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py @@ -1,3 +1,31 @@ +# 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 -- Gitee From 195976f53bff13e0b6c7cbb73d92ace54d07081e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:04:45 +0000 Subject: [PATCH 09/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../contrib/cv/SRFBN_for_TensorFlow/test.py | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py index 57457c50a..66419630c 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py @@ -1,3 +1,30 @@ +# 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 -- Gitee From 360200965be0acf509adf0d3288d13c64d02f0df Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:05:06 +0000 Subject: [PATCH 10/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../contrib/cv/SRFBN_for_TensorFlow/train.py | 28 ++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py index 58a4e6660..58e3a8272 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py @@ -1,4 +1,30 @@ - +# 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 * -- Gitee From fe9961e780567f1b208fe46c23ffa896673da98f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:05:28 +0000 Subject: [PATCH 11/26] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SRFBN=5Ffor=5FTensorFlow/traditional=5Fblur?= =?UTF-8?q?.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../SRFBN_for_TensorFlow/traditional_blur.py | 508 ------------------ 1 file changed, 508 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py deleted file mode 100644 index 0d83abd5e..000000000 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/traditional_blur.py +++ /dev/null @@ -1,508 +0,0 @@ -from npu_bridge.npu_init import * -import numpy as np -import scipy, cv2 -from scipy import fftpack - - -''' -modified by Kai Zhang (github: https://github.com/cszn) -03/03/2019 -''' - - -# -------------------------------- -# get rho and sigma -# -------------------------------- -def get_rho_sigma(sigma=2.55/255, iter_num=15): - ''' - Kai Zhang (github: https://github.com/cszn) - 03/03/2019 - ''' - modelSigma1 = 49.0 - modelSigma2 = 2.55 - modelSigmaS = np.logspace(np.log10(modelSigma1), np.log10(modelSigma2), iter_num) - sigmas = modelSigmaS/255. - mus = list(map(lambda x: (sigma**2)/(x**2)/3, sigmas)) - rhos = mus - return rhos, sigmas - - -# -------------------------------- -# HWC, get uperleft and denominator -# -------------------------------- -def get_uperleft_denominator(img, kernel): - ''' - Kai Zhang (github: https://github.com/cszn) - 03/03/2019 - ''' - V = psf2otf(kernel, img.shape[:2]) # discrete fourier transform of kernel - denominator = np.expand_dims(np.abs(V)**2, axis=2) # Fourier transform of K transpose * K - upperleft = np.expand_dims(np.conj(V), axis=2) * np.fft.fft2(img, axes=[0, 1]) - return upperleft, denominator - - -# otf2psf: not sure where I got this one from. Maybe translated from Octave source code or whatever. It's just math. -def otf2psf(otf, outsize=None): - insize = np.array(otf.shape) - psf = np.fft.ifftn(otf, axes=(0, 1)) - for axis, axis_size in enumerate(insize): - psf = np.roll(psf, np.floor(axis_size / 2).astype(int), axis=axis) - if type(outsize) != type(None): - insize = np.array(otf.shape) - outsize = np.array(outsize) - n = max(np.size(outsize), np.size(insize)) - # outsize = postpad(outsize(:), n, 1); - # insize = postpad(insize(:) , n, 1); - colvec_out = outsize.flatten().reshape((np.size(outsize), 1)) - colvec_in = insize.flatten().reshape((np.size(insize), 1)) - outsize = np.pad(colvec_out, ((0, max(0, n - np.size(colvec_out))), (0, 0)), mode="constant") - insize = np.pad(colvec_in, ((0, max(0, n - np.size(colvec_in))), (0, 0)), mode="constant") - - pad = (insize - outsize) / 2 - if np.any(pad < 0): - print("otf2psf error: OUTSIZE must be smaller than or equal than OTF size") - prepad = np.floor(pad) - postpad = np.ceil(pad) - dims_start = prepad.astype(int) - dims_end = (insize - postpad).astype(int) - for i in range(len(dims_start.shape)): - psf = np.take(psf, range(dims_start[i][0], dims_end[i][0]), axis=i) - n_ops = np.sum(otf.size * np.log2(otf.shape)) - psf = np.real_if_close(psf, tol=n_ops) - return psf - - -# psf2otf copied/modified from https://github.com/aboucaud/pypher/blob/master/pypher/pypher.py -def psf2otf(psf, shape=None): - """ - Convert point-spread function to optical transfer function. - Compute the Fast Fourier Transform (FFT) of the point-spread - function (PSF) array and creates the optical transfer function (OTF) - array that is not influenced by the PSF off-centering. - By default, the OTF array is the same size as the PSF array. - To ensure that the OTF is not altered due to PSF off-centering, PSF2OTF - post-pads the PSF array (down or to the right) with zeros to match - dimensions specified in OUTSIZE, then circularly shifts the values of - the PSF array up (or to the left) until the central pixel reaches (1,1) - position. - Parameters - ---------- - psf : `numpy.ndarray` - PSF array - shape : int - Output shape of the OTF array - Returns - ------- - otf : `numpy.ndarray` - OTF array - Notes - ----- - Adapted from MATLAB psf2otf function - """ - if type(shape) == type(None): - shape = psf.shape - shape = np.array(shape) - if np.all(psf == 0): - # return np.zeros_like(psf) - return np.zeros(shape) - if len(psf.shape) == 1: - psf = psf.reshape((1, psf.shape[0])) - inshape = psf.shape - psf = zero_pad(psf, shape, position='corner') - for axis, axis_size in enumerate(inshape): - psf = np.roll(psf, -int(axis_size / 2), axis=axis) - # Compute the OTF - otf = np.fft.fft2(psf, axes=(0, 1)) - # Estimate the rough number of operations involved in the FFT - # and discard the PSF imaginary part if within roundoff error - # roundoff error = machine epsilon = sys.float_info.epsilon - # or np.finfo().eps - n_ops = np.sum(psf.size * np.log2(psf.shape)) - otf = np.real_if_close(otf, tol=n_ops) - return otf - - -def zero_pad(image, shape, position='corner'): - """ - Extends image to a certain size with zeros - Parameters - ---------- - image: real 2d `numpy.ndarray` - Input image - shape: tuple of int - Desired output shape of the image - position : str, optional - The position of the input image in the output one: - * 'corner' - top-left corner (default) - * 'center' - centered - Returns - ------- - padded_img: real `numpy.ndarray` - The zero-padded image - """ - shape = np.asarray(shape, dtype=int) - imshape = np.asarray(image.shape, dtype=int) - if np.alltrue(imshape == shape): - return image - if np.any(shape <= 0): - raise ValueError("ZERO_PAD: null or negative shape given") - dshape = shape - imshape - if np.any(dshape < 0): - raise ValueError("ZERO_PAD: target size smaller than source one") - pad_img = np.zeros(shape, dtype=image.dtype) - idx, idy = np.indices(imshape) - if position == 'center': - if np.any(dshape % 2 != 0): - raise ValueError("ZERO_PAD: source and target shapes " - "have different parity.") - offx, offy = dshape // 2 - else: - offx, offy = (0, 0) - pad_img[idx + offx, idy + offy] = image - return pad_img - - -''' -Reducing boundary artifacts -''' - - -def opt_fft_size(n): - ''' - Kai Zhang (github: https://github.com/cszn) - 03/03/2019 - # opt_fft_size.m - # compute an optimal data length for Fourier transforms - # written by Sunghyun Cho (sodomau@postech.ac.kr) - # persistent opt_fft_size_LUT; - ''' - - LUT_size = 2048 - # print("generate opt_fft_size_LUT") - opt_fft_size_LUT = np.zeros(LUT_size) - - e2 = 1 - while e2 <= LUT_size: - e3 = e2 - while e3 <= LUT_size: - e5 = e3 - while e5 <= LUT_size: - e7 = e5 - while e7 <= LUT_size: - if e7 <= LUT_size: - opt_fft_size_LUT[e7-1] = e7 - if e7*11 <= LUT_size: - opt_fft_size_LUT[e7*11-1] = e7*11 - if e7*13 <= LUT_size: - opt_fft_size_LUT[e7*13-1] = e7*13 - e7 = e7 * 7 - e5 = e5 * 5 - e3 = e3 * 3 - e2 = e2 * 2 - - nn = 0 - for i in range(LUT_size, 0, -1): - if opt_fft_size_LUT[i-1] != 0: - nn = i-1 - else: - opt_fft_size_LUT[i-1] = nn+1 - - m = np.zeros(len(n)) - for c in range(len(n)): - nn = n[c] - if nn <= LUT_size: - m[c] = opt_fft_size_LUT[nn-1] - else: - m[c] = -1 - return m - - -def wrap_boundary_liu(img, img_size): - - """ - Reducing boundary artifacts in image deconvolution - Renting Liu, Jiaya Jia - ICIP 2008 - """ - if img.ndim == 2: - ret = wrap_boundary(img, img_size) - elif img.ndim == 3: - ret = [wrap_boundary(img[:, :, i], img_size) for i in range(3)] - ret = np.stack(ret, 2) - return ret - - -def wrap_boundary(img, img_size): - - """ - python code from: - https://github.com/ys-koshelev/nla_deblur/blob/90fe0ab98c26c791dcbdf231fe6f938fca80e2a0/boundaries.py - Reducing boundary artifacts in image deconvolution - Renting Liu, Jiaya Jia - ICIP 2008 - """ - (H, W) = np.shape(img) - H_w = int(img_size[0]) - H - W_w = int(img_size[1]) - W - - # ret = np.zeros((img_size[0], img_size[1])); - alpha = 1 - HG = img[:, :] - - r_A = np.zeros((alpha*2+H_w, W)) - r_A[:alpha, :] = HG[-alpha:, :] - r_A[-alpha:, :] = HG[:alpha, :] - a = np.arange(H_w)/(H_w-1) - # r_A(alpha+1:end-alpha, 1) = (1-a)*r_A(alpha,1) + a*r_A(end-alpha+1,1) - r_A[alpha:-alpha, 0] = (1-a)*r_A[alpha-1, 0] + a*r_A[-alpha, 0] - # r_A(alpha+1:end-alpha, end) = (1-a)*r_A(alpha,end) + a*r_A(end-alpha+1,end) - r_A[alpha:-alpha, -1] = (1-a)*r_A[alpha-1, -1] + a*r_A[-alpha, -1] - - r_B = np.zeros((H, alpha*2+W_w)) - r_B[:, :alpha] = HG[:, -alpha:] - r_B[:, -alpha:] = HG[:, :alpha] - a = np.arange(W_w)/(W_w-1) - r_B[0, alpha:-alpha] = (1-a)*r_B[0, alpha-1] + a*r_B[0, -alpha] - r_B[-1, alpha:-alpha] = (1-a)*r_B[-1, alpha-1] + a*r_B[-1, -alpha] - - if alpha == 1: - A2 = solve_min_laplacian(r_A[alpha-1:, :]) - B2 = solve_min_laplacian(r_B[:, alpha-1:]) - r_A[alpha-1:, :] = A2 - r_B[:, alpha-1:] = B2 - else: - A2 = solve_min_laplacian(r_A[alpha-1:-alpha+1, :]) - r_A[alpha-1:-alpha+1, :] = A2 - B2 = solve_min_laplacian(r_B[:, alpha-1:-alpha+1]) - r_B[:, alpha-1:-alpha+1] = B2 - A = r_A - B = r_B - - r_C = np.zeros((alpha*2+H_w, alpha*2+W_w)) - r_C[:alpha, :] = B[-alpha:, :] - r_C[-alpha:, :] = B[:alpha, :] - r_C[:, :alpha] = A[:, -alpha:] - r_C[:, -alpha:] = A[:, :alpha] - - if alpha == 1: - C2 = C2 = solve_min_laplacian(r_C[alpha-1:, alpha-1:]) - r_C[alpha-1:, alpha-1:] = C2 - else: - C2 = solve_min_laplacian(r_C[alpha-1:-alpha+1, alpha-1:-alpha+1]) - r_C[alpha-1:-alpha+1, alpha-1:-alpha+1] = C2 - C = r_C - # return C - A = A[alpha-1:-alpha-1, :] - B = B[:, alpha:-alpha] - C = C[alpha:-alpha, alpha:-alpha] - ret = np.vstack((np.hstack((img, B)), np.hstack((A, C)))) - return ret - - -def solve_min_laplacian(boundary_image): - (H, W) = np.shape(boundary_image) - - # Laplacian - f = np.zeros((H, W)) - # boundary image contains image intensities at boundaries - boundary_image[1:-1, 1:-1] = 0 - j = np.arange(2, H)-1 - k = np.arange(2, W)-1 - f_bp = np.zeros((H, W)) - f_bp[np.ix_(j, k)] = -4*boundary_image[np.ix_(j, k)] + boundary_image[np.ix_(j, k+1)] + boundary_image[np.ix_(j, k-1)] + boundary_image[np.ix_(j-1, k)] + boundary_image[np.ix_(j+1, k)] - - del(j, k) - f1 = f - f_bp # subtract boundary points contribution - del(f_bp, f) - - # DST Sine Transform algo starts here - f2 = f1[1:-1,1:-1] - del(f1) - - # compute sine tranform - if f2.shape[1] == 1: - tt = fftpack.dst(f2, type=1, axis=0)/2 - else: - tt = fftpack.dst(f2, type=1)/2 - - if tt.shape[0] == 1: - f2sin = np.transpose(fftpack.dst(np.transpose(tt), type=1, axis=0)/2) - else: - f2sin = np.transpose(fftpack.dst(np.transpose(tt), type=1)/2) - del(f2) - - # compute Eigen Values - [x, y] = np.meshgrid(np.arange(1, W-1), np.arange(1, H-1)) - denom = (2*np.cos(np.pi*x/(W-1))-2) + (2*np.cos(np.pi*y/(H-1)) - 2) - - # divide - f3 = f2sin/denom - del(f2sin, x, y) - - # compute Inverse Sine Transform - if f3.shape[0] == 1: - tt = fftpack.idst(f3*2, type=1, axis=1)/(2*(f3.shape[1]+1)) - else: - tt = fftpack.idst(f3*2, type=1, axis=0)/(2*(f3.shape[0]+1)) - del(f3) - if tt.shape[1] == 1: - img_tt = np.transpose(fftpack.idst(np.transpose(tt)*2, type=1)/(2*(tt.shape[0]+1))) - else: - img_tt = np.transpose(fftpack.idst(np.transpose(tt)*2, type=1, axis=0)/(2*(tt.shape[1]+1))) - del(tt) - - # put solution in inner points; outer points obtained from boundary image - img_direct = boundary_image - img_direct[1:-1, 1:-1] = 0 - img_direct[1:-1, 1:-1] = img_tt - return img_direct - - -""" -Created on Thu Jan 18 15:36:32 2018 -@author: italo -https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py -""" - -""" -Syntax -h = fspecial(type) -h = fspecial('average',hsize) -h = fspecial('disk',radius) -h = fspecial('gaussian',hsize,sigma) -h = fspecial('laplacian',alpha) -h = fspecial('log',hsize,sigma) -h = fspecial('motion',len,theta) -h = fspecial('prewitt') -h = fspecial('sobel') -""" - - -def fspecial_average(hsize=3): - """Smoothing filter""" - return np.ones((hsize, hsize))/hsize**2 - - -def fspecial_disk(radius): - """Disk filter""" - # raise(NotImplemented) - # rad = 0.6 - rad = radius - crad = np.ceil(rad-0.5) - [x, y] = np.meshgrid(np.arange(-crad, crad+1), np.arange(-crad, crad+1)) - maxxy = np.zeros(x.shape) - maxxy[abs(x) >= abs(y)] = abs(x)[abs(x) >= abs(y)] - maxxy[abs(y) >= abs(x)] = abs(y)[abs(y) >= abs(x)] - minxy = np.zeros(x.shape) - minxy[abs(x) <= abs(y)] = abs(x)[abs(x) <= abs(y)] - minxy[abs(y) <= abs(x)] = abs(y)[abs(y) <= abs(x)] - m1 = (rad**2 < (maxxy+0.5)**2 + (minxy-0.5)**2)*(minxy-0.5) +\ - (rad**2 >= (maxxy+0.5)**2 + (minxy-0.5)**2)*\ - np.sqrt((rad**2 + 0j) - (maxxy + 0.5)**2) - m2 = (rad**2 > (maxxy-0.5)**2 + (minxy+0.5)**2)*(minxy+0.5) +\ - (rad**2 <= (maxxy-0.5)**2 + (minxy+0.5)**2)*\ - np.sqrt((rad**2 + 0j) - (maxxy - 0.5)**2) - # sgrid = (rad**2 * (0.5*(asin(m2/rad) - asin(m1/rad)) + 0.25*(sin(2*asin(m2/rad)) - ) - h = None - return h - - -def fspecial_gaussian(hsize, sigma): - hsize = [hsize, hsize] - siz = [(hsize[0]-1.0)/2.0, (hsize[1]-1.0)/2.0] - std = sigma - [x, y] = np.meshgrid(np.arange(-siz[1], siz[1]+1), np.arange(-siz[0], siz[0]+1)) - arg = -(x*x + y*y)/(2*std*std) - h = np.exp(arg) - h[h < scipy.finfo(float).eps * h.max()] = 0 - sumh = h.sum() - if sumh != 0: - h = h/sumh - return h - - - -def fspecial(filter_type, *args, **kwargs): - ''' - python code from: - https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py - ''' - if filter_type == 'average': - return fspecial_average(*args, **kwargs) - if filter_type == 'disk': - return fspecial_disk(*args, **kwargs) - if filter_type == 'gaussian': - return fspecial_gaussian(*args, **kwargs) - - -def traditional_blur(img, filter_type): - ksize = np.random.choice([3, 5], 1) - # ksize = 3 - if filter_type == 'average': - img = np.array(img, np.float32) - blur_img = cv2.medianBlur(img, ksize=ksize) - return blur_img - if filter_type == 'gaussian': - blur_img = cv2.GaussianBlur(img, ksize=(ksize, ksize), sigmaX=0, sigmaY=0) - return blur_img - -def motion_blur(img, angle=45): - degree = np.random.randint(1, 8, 1) # 8模糊度已经足够，轻重模糊随机 - image = np.array(img) - - # 这里生成任意角度的运动模糊kernel的矩阵， degree越大，模糊程度越高 - M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1) - motion_blur_kernel = np.diag(np.ones(degree)) - motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (degree, degree)) - - motion_blur_kernel = motion_blur_kernel / degree - blurred = cv2.filter2D(image, -1, motion_blur_kernel) - - # convert to uint8 - cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX) - blurred = (blurred-127.5)/128 - return blurred - - -if __name__ == '__main__': - a = opt_fft_size([111]) - print(a) - - print(fspecial('gaussian', 5, 1)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- Gitee From d0244153372cb6141624ff3e6fd8d745deda7ba4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:05:36 +0000 Subject: [PATCH 12/26] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Te?= =?UTF-8?q?nsorFlow/contrib/cv/SRFBN=5Ffor=5FTensorFlow/psnr=5Fssim.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../cv/SRFBN_for_TensorFlow/psnr_ssim.py | 86 ------------------- 1 file changed, 86 deletions(-) delete mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py deleted file mode 100644 index c27924230..000000000 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/psnr_ssim.py +++ /dev/null @@ -1,86 +0,0 @@ -import numpy as np -import cv2 - -#灰度图psnr -def compare_psnr_gray(im1,im2,maxI=255): - im1=im1.astype(np.float64) - im2=im2.astype(np.float64) - diff=im1-im2 - mse=np.mean(np.square(diff)) - if mse==0: return float('inf') - return 10*np.log10(maxI*maxI/mse) - -#彩色图psnr -def compare_psnr_rgb(im1,im2,maxI=255): - blue1,green1,red1=cv2.split(im1) - blue2,green2,red2=cv2.split(im2) - psnr_blue=compare_psnr_gray(blue1,blue2,maxI) - psnr_green=compare_psnr_gray(green1,green2,maxI) - psnr_red=compare_psnr_gray(red1,red2,maxI) - - #三通道取平均值 - return (psnr_blue+psnr_green+psnr_red)/3 - -#可以直接用的（不能把灰度图和彩色图直接比较） -def compare_psnr(im1,im2,maxI=255): - #如果两张图片大小不同或者同为同一类型（灰度、彩色）图就报错 - if im1.shape!=im2.shape: raise ValueError("错误：图片大小维度不同") - if (im1.ndim==2) and (im2.ndim==2): return compare_psnr_gray(im1,im2) - #彩色图里可能有单通道（？） - elif (im1.ndim==3) and (im2.ndim==3): - if im1.shape[2]==3: - return compare_psnr_rgb(im1,im2) - elif im1.shape[2]==1: - return compare_psnr_gray(np.squeeze(im1),np.squeeze(im2)) - else: raise ValueError("错误：错误的图片维度") - -#ssim -def ssim(im1,im2,maxI=255): - #0.01和0.03是已经定下来的常数，不要改 - c1=(0.01*maxI)**2 - c2=(0.03*maxI)**2 - - #转换成float64类型 - im1=im1.astype(np.float64) - im2=im2.astype(np.float64) - #高斯核，这里11和1.5也是定下来的不要改 - kernel=cv2.getGaussianKernel(11,1.5) - window=np.outer(kernel,kernel.transpose()) - - #求卷积 - #ssim先将图片分成若干个小块后分别按照公式的各元素求各种卷积 - mu1=cv2.filter2D(im1,-1,window)[5:-5,5:-5] - mu2=cv2.filter2D(im2,-1,window)[5:-5,5:-5] - mu1_sq=mu1**2 - mu2_sq=mu2**2 - mu1_mu2=mu1*mu2 - sigma1_sq=cv2.filter2D(im1**2,-1,window)[5:-5,5:-5]-mu1_sq - sigma2_sq=cv2.filter2D(im2**2,-1,window)[5:-5,5:-5]-mu2_sq - sigma12=cv2.filter2D(im1*im2,-1,window)[5:-5,5:-5]-mu1_mu2 - - #ssim的计算公式 - ssim_map=((2*mu1_mu2+c1)*(2*sigma12+c2))/((mu1_sq+mu2_sq+c1)*(sigma1_sq+sigma2_sq+c2)) - #取所有小块计算结果的平均值 - return ssim_map.mean() - -#可以直接用的 -def compare_ssim(im1,im2,maxI=255): - #如果两张图片大小不同或者同为同一类型（灰度、彩色）图就报错 - if im1.shape!=im2.shape: - raise ValueError("错误：图片维度大小不同") - if im1.ndim==2: - return ssim(im1,im2) - #彩色图里可能有单通道（？） - elif im1.ndim==3: - if im1.shape[2]==3: - blue1,green1,red1=cv2.split(im1) - blue2,green2,red2=cv2.split(im2) - ssim_blue=ssim(blue1,blue2) - ssim_green=ssim(green1,green2) - ssim_red=ssim(red1,red2) - - #同psnr，取平均值 - return (ssim_blue+ssim_green+ssim_red)/3 - elif im1.shape[2]==1: - return ssim(np.squeeze(im1),np.squeeze(im2)) - else: raise ValueError("错误：错误的图片维度") \ No newline at end of file -- Gitee From e462aac70a762a8e0db7e91d6a47a907f60960de Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:07:36 +0000 Subject: [PATCH 13/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md index 79c405514..c322a7d11 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md @@ -94,15 +94,13 @@ SRFBN是采取反馈连接来提高重建超分辨率图片效果的网络模型 ├── README.md //代码说明文档 ├── config.py //模型配置代码 ├── PreProcess.py //数据预处理代码 -├── psnr_ssim.py //图像质量评估代码 ├── requirements.txt //训练python依赖列表 ├── SRFBN_model.py //SRFBN网络模型代码 ├── test.py //测试代码 -├── traditional_blur.py //图像模糊处理代码 ├── train.py //训练代码 ├── test │ ├──train_performance_1p.sh //单卡训练验证性能启动脚本 - +│ ├──train_full_1p.sh //单卡全量训练启动脚本 ``` ## 脚本参数 -- Gitee From 777f0cf88bb72295a0b0e7fb1cfc598c9ecf97b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:11:40 +0000 Subject: [PATCH 14/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/PreProcess.py | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py index 0d13c7cac..2d34ac1ab 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py @@ -33,17 +33,17 @@ import random #from skimage import util #给图像数据添加噪声 -#def add_noise(img): - # mode_types = ['gaussian', 'localvar', 'poisson', 'speckle'] # 'salt', 'pepper', 's&p'这三个噪声太假了 - # inx = int(np.random.choice(np.arange(len(mode_types)), 1)) - # # inx = 0 - # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)#转换色彩空间为RGB - # mean = random.random() * 0.001 # + 0.001#random.random()生成0到1之间的随机数 - # var = random.random() * 0.002 # + 0.01 - # noise_img = util.random_noise(img.copy(), mode=mode_types[inx], - # mean=mean, - # var=var)#添加噪声 - # return noise_img +def add_noise(img): + mode_types = ['gaussian', 'localvar', 'poisson', 'speckle'] # 'salt', 'pepper', 's&p'这三个噪声太假了 + inx = int(np.random.choice(np.arange(len(mode_types)), 1)) + # inx = 0 + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)#转换色彩空间为RGB + mean = random.random() * 0.001 # + 0.001#random.random()生成0到1之间的随机数 + var = random.random() * 0.002 # + 0.01 + noise_img = util.random_noise(img.copy(), mode=mode_types[inx], + mean=mean, + var=var)#添加噪声 + return noise_img #数据扩充或增强 def augment_data(img_patch, flip, rot): # img_patchs : n,h,w,c -- Gitee From 85a53505fcb7366fec6ae7a469bb4516b54fba99 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 04:11:57 +0000 Subject: [PATCH 15/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py index 2d34ac1ab..6afecec2f 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py @@ -30,7 +30,7 @@ from npu_bridge.npu_init import * import cv2 import numpy as np import random -#from skimage import util +from skimage import util #给图像数据添加噪声 def add_noise(img): -- Gitee From 05a73ba80be4154c1b9b60075452e7c80488c4db Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 16:49:45 +0000 Subject: [PATCH 16/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/Basic_Model.py | 56 +++++++++---------- 1 file changed, 27 insertions(+), 29 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py index 3051156e8..700d4e99d 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/Basic_Model.py @@ -32,39 +32,37 @@ from tensorflow.contrib import layers import sys class basic_network(object): - #初始化网络配置 def __init__(self, cfg): self.training=True self.cfg = cfg - self.params_count = 0#参数数量 - #初始化参数 + self.params_count = 0 #the amount of parameters def init_params(self, *args, **kwargs): def _variable_on_cpu(w_shape, b_shape, weight_decay=0.99, use_bias=True, name="conv"): - with tf.device('/cpu:0'):#使用cpu - w = tf.Variable(tf.truncated_normal(w_shape, 0.0, 0.001), trainable=True, name="%s_w" % name)#权重 - tf.add_to_collection(name="weights_l2_loss", value=self.calc_l1_loss(w, weight_decay))#将value加入name为'weights_l2_loss'的列表中 - b = tf.Variable(tf.zeros(b_shape), trainable=use_bias, name="%s_b" % name)#偏置 - return w, b #返回参数w和b - kernel_size = kwargs["kernel_size"]#kwargs为字典 + with tf.device('/cpu:0'): + w = tf.Variable(tf.truncated_normal(w_shape, 0.0, 0.001), trainable=True, name="%s_w" % name) + tf.add_to_collection(name="weights_l2_loss", value=self.calc_l1_loss(w, weight_decay)) + b = tf.Variable(tf.zeros(b_shape), trainable=use_bias, name="%s_b" % name) + return w, b + kernel_size = kwargs["kernel_size"] in_channels = kwargs["in_channels"] out_channels = kwargs["out_channels"] # weight_decay = kwargs["weight_decay"] - w_shape = [kernel_size, kernel_size, in_channels, out_channels]#权重的size - b_shape = [out_channels]#b的size + w_shape = [kernel_size, kernel_size, in_channels, out_channels] + b_shape = [out_channels] name = kwargs["name"] self.params_count += kernel_size*kernel_size*in_channels*out_channels - self.params_count += out_channels#参数的数量 - return _variable_on_cpu(w_shape, b_shape, use_bias=kwargs["use_bias"], name=name)#返回初始化后的w和b - #计算代价函数，L1和L2 + self.params_count += out_channels + return _variable_on_cpu(w_shape, b_shape, use_bias=kwargs["use_bias"], name=name) + def calc_loss(self, *args, **kwargs): loss_type = kwargs["loss_type"] x = kwargs["x"] y = kwargs["y"] - if loss_type == "L1":#代价函数类型 + if loss_type == "L1": return tf.reduce_sum(tf.abs(x-y), name="L1_loss") elif loss_type == "L2": return tf.nn.l2_loss((x-y), name="L2_loss") - #激活函数 + def activation(self, *args, **kwargs): act_type = kwargs["act_type"] act_type = act_type.lower() @@ -80,31 +78,31 @@ class basic_network(object): return tf.nn.tanh(args[0]) else: return args[0] - #计算L2型代价函数 + def calc_l2_loss(self, weight, weight_decay): - _, _, _, outchannel = weight.get_shape().as_list()#这里只需要获取输出的channel数 + _, _, _, outchannel = weight.get_shape().as_list() return (weight_decay) * tf.reduce_sum(tf.square(weight)) / outchannel - #计算L1型代价函数 + def calc_l1_loss(self, weight, weight_decay): _, _, _, outchannel = weight.get_shape().as_list() return (weight_decay)*tf.reduce_sum(tf.abs(weight)) / outchannel - #批归一化处理 + def batch_norm(self, *args, **kwargs): - return tf.layers.batch_normalization(args[0], training=kwargs["training"])#第一个参数是输入 - #归一化的一种方法 + return tf.layers.batch_normalization(args[0], training=kwargs["training"]) + def instance_norm(self, *args, **kwargs): return layers.instance_norm(args[0], kwargs["name"]) - #激活函数的一种 + def hard_sigmoid(self, x): return tf.nn.relu6((x+3)/6) def hard_swish(self, x): return x * self.hard_sigmoid(x) - #平均池化 + def global_average_pooling(self, x, name="GAP"): return tf.reduce_mean(x, axis=[1, 2], keep_dims=True, name="Global_Average_Pooling_%s" % name)#不降维 - #定义卷积块 + def ConvBlock(self,x, in_channels, out_channels, kernel_size, stride=1, name="ConvBlock", BN=True, use_bias=True, padding="VALID", act_type="relu", mode="CNA"): @@ -112,7 +110,7 @@ class basic_network(object): assert (mode in ['CNA', 'NAC']), '[ERROR] Wrong mode in [%s]!' % sys.modules[__name__]#断言 weight, bias = self.init_params(kernel_size=kernel_size, in_channels=in_channels, out_channels=out_channels, use_bias=use_bias, name=name) - if mode == "CNA":#先卷积再归一化再激活 + if mode == "CNA": x = tf.nn.conv2d(x, filter=weight, strides=[1, stride, stride, 1], padding=padding) x = tf.nn.bias_add(x, bias) if BN: @@ -136,15 +134,15 @@ class basic_network(object): x = tf.nn.conv2d(x, filter=weight, strides=[1, stride, stride, 1], padding=padding) x = tf.nn.bias_add(x, bias) return x - #反卷积块（上采样upsampling） + def DeConvBlock(self, x, in_channels, out_channels, kernel_size, stride=1, name="DeConvBlock", BN=True, use_bias=True, padding="VALID", act_type="relu", mode="CNA"): assert (mode in ['CNA', 'NAC']), '[ERROR] Wrong mode in [%s]!' % sys.modules[__name__] - b, h, w, c = x.get_shape().as_list()#h为图像高度，w为宽度，b为batch，即一次处理的样本数，c为颜色通道数 + b, h, w, c = x.get_shape().as_list() out_shape = [b, h * self.cfg.scale, w * self.cfg.scale, out_channels] weight, bias = self.init_params(kernel_size=kernel_size, in_channels=out_channels, out_channels=in_channels, use_bias=use_bias, name=name) - if mode == "CNA":#反卷积函数（扩大图像尺寸）（其实也是卷积） + if mode == "CNA": x = tf.nn.conv2d_transpose(x, filter=weight, output_shape=out_shape, strides=[1, stride, stride, 1], padding=padding) x = tf.nn.bias_add(x, bias) -- Gitee From 5ebe5d786814f2380ba1717d91d7d184d449119b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 16:52:53 +0000 Subject: [PATCH 17/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/PreProcess.py | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py index 6afecec2f..5ed011cdb 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/PreProcess.py @@ -32,38 +32,38 @@ import numpy as np import random from skimage import util -#给图像数据添加噪声 + def add_noise(img): - mode_types = ['gaussian', 'localvar', 'poisson', 'speckle'] # 'salt', 'pepper', 's&p'这三个噪声太假了 + mode_types = ['gaussian', 'localvar', 'poisson', 'speckle'] # 'salt', 'pepper', 's&p' is too fake inx = int(np.random.choice(np.arange(len(mode_types)), 1)) - # inx = 0 - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)#转换色彩空间为RGB - mean = random.random() * 0.001 # + 0.001#random.random()生成0到1之间的随机数 + inx = 0 + img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB) + mean = random.random() * 0.001 # + 0.001#random.random() generates number between 0 and 1 var = random.random() * 0.002 # + 0.01 noise_img = util.random_noise(img.copy(), mode=mode_types[inx], mean=mean, - var=var)#添加噪声 + var=var) return noise_img -#数据扩充或增强 + def augment_data(img_patch, flip, rot): # img_patchs : n,h,w,c if flip==1: - img_patch = img_patch[:, ::-1, :] # hflip#水平翻转 + img_patch = img_patch[:, ::-1, :] # hflip elif flip==2: - img_patch = img_patch[::-1, :, :] # vflip#垂直翻转 + img_patch = img_patch[::-1, :, :] # vflip if rot==1: - img_patch = cv2.rotate(img_patch, cv2.ROTATE_90_CLOCKWISE)#顺时针旋转90 + img_patch = cv2.rotate(img_patch, cv2.ROTATE_90_CLOCKWISE) elif rot==2: img_patch = cv2.rotate(img_patch, cv2.ROTATE_90_COUNTERCLOCKWISE) return img_patch -#预处理数据 + def preprocess(imgs, cfg): LR_patchs, HR_patchs = [], [] for img in imgs: - HR = cv2.imread(img.strip(), cv2.IMREAD_COLOR)#读取图片路径，并以RGB模式 - HR = (HR - 127.5) / 128#归一化 - h, w, c = HR.shape#高度，宽度，颜色通道数 + HR = cv2.imread(img.strip(), cv2.IMREAD_COLOR) + HR = (HR - 127.5) / 128 + h, w, c = HR.shape x_stride = w // (cfg.imagesize * cfg.scale) y_stride = h // (cfg.imagesize * cfg.scale) -- Gitee From e9a93bb612a6a6e80feeb91fb92e85d7dfe76810 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 16:55:28 +0000 Subject: [PATCH 18/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py index 1ce90c923..4afaf9bb5 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/SRFBN_model.py @@ -43,7 +43,7 @@ class SRFBN(basic_network): self.last_hidden = None self.should_reset = True self.outs = [] - #FB块 + #FB block that forwardpros feedback information def FeedBackBlock(self, x, num_features, num_groups, act_type, name="FBB"): if self.cfg.scale == 1: stride = 1 @@ -110,7 +110,7 @@ class SRFBN(basic_network): stride = 4 padding = "SAME" kernel_size = 8 - # x = self.sub_mean(self.imageplaceholder) # 暂且当作归一化 + # x = self.sub_mean(self.imageplaceholder) # normalize _, height, width, _ = self.imageplaceholder.get_shape().as_list() @@ -136,7 +136,7 @@ class SRFBN(basic_network): # t = t + inter_res # t = self.add_mean(t) self.outs.append(t) - #训练步骤 + def train_step(self): self.build() print("This Net has Params num is %f MB" % (self.params_count * 4 / 1024 / 1024)) # float32 @@ -156,7 +156,7 @@ class SRFBN(basic_network): self.merged_summary = tf.summary.merge_all() self.saver = tf.train.Saver(max_to_keep=1) - #加载检查点 + #loading ckpt def load(self): model_name = "SRFBN.model" model_dir = "%s_%s_%s_%s_c%d_x%s" % ( @@ -173,7 +173,7 @@ class SRFBN(basic_network): print("\nCheckpoint Loading Failed! \n") return step - #保存当前模型 + #save model def save(self, step): model_name = "SRFBN.model" model_dir = "%s_%s_%s_%s_c%d_x%s" % \ @@ -187,7 +187,7 @@ class SRFBN(basic_network): self.saver.save(self.sess, os.path.join(checkpoint_dir, model_name), global_step=step) - #测试 + #test def test(self, width, height): self.cfg.batchsize = 1 testshape = [self.cfg.batchsize, height, width, self.cfg.c_dim] -- Gitee From 271fa9374abac531090ce2e9b5896f690e912310 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 16:58:52 +0000 Subject: [PATCH 19/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../contrib/cv/SRFBN_for_TensorFlow/config.py | 36 +++++++++---------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py index 4da2aec8f..391bbadcc 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/config.py @@ -31,15 +31,15 @@ import os class config: def __init__(self): - self.batchsize = 1#一次处理的样本数量 - self.Process_num = 3 #进程数量 - self.maxsize = 200 #最大的大小 - self.ngpu = 1 #gpu数量 - self.imagesize = 64#图片大小 - self.scale = 3#缩放规模 - self.epoch = 1000#迭代次数 - #创建检查点，记录，结果目录 - self.checkpoint_dir = "./model"#检查点目录 + self.batchsize = 1 + self.Process_num = 3 + self.maxsize = 200 + self.ngpu = 1 + self.imagesize = 64 + self.scale = 3 + self.epoch = 1000 + #create ckpt,log,result dir + self.checkpoint_dir = "./model" if not os.path.exists(self.checkpoint_dir): os.mkdir(self.checkpoint_dir) self.log_dir = "./log" @@ -54,24 +54,24 @@ class config: class SRFBN_config(config): def __init__(self): super(SRFBN_config, self).__init__() - self.istrain = True#正在训练还是正在测试 + self.istrain = True#is train or is test self.istest = not self.istrain - self.c_dim = 3 #color channel 可以训练灰度图也可以训练RGB图 + self.c_dim = 3 #color channel can train one-channel pic or RGB pic self.in_channels = 3 self.out_channels = 3 self.num_features = 32#base number of filter - self.num_steps = 4#时间步 - self.num_groups = 6#FBB中feedbackblock中projection group数量 - self.BN = True# + self.num_steps = 4# timestep + self.num_groups = 6#the number of projection group of FBB feedbackblock + self.BN = True if self.BN: self.BN_type = "BN" # "BN" # or "IN" self.act_type = "prelu" #activation function self.loss_type = "L2" - self.lr_steps = [150, 300, 550, 750]#迭代次数表 - self.lr_gama = 1#参数 - self.learning_rate = 2e-7#学习率 + self.lr_steps = [150, 300, 550, 750]#iteration + self.lr_gama = 1 + self.learning_rate = 2e-7#learning rate self.load_premodel = True - #创建目录 + #create dir self.srfbn_logdir = "%s/srfbn" % self.log_dir if not os.path.exists(self.srfbn_logdir): os.mkdir(self.srfbn_logdir) -- Gitee From 579bd4ef7bed2567aa55ffa0f9df139b6f7b7f05 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 17:03:02 +0000 Subject: [PATCH 20/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py index 58e3a8272..46f8808e3 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/train.py @@ -43,7 +43,7 @@ def train_SRFBN(dataset, sess, cfg): srfbn.train_step() out = tf.add_n(srfbn.outs) / srfbn.cfg.num_steps ## build Optimizer - #使学习率在不同迭代阶段不同 + #make lr_rate different in different stages boundaries = [len(dataset)*epoch//cfg.batchsize for epoch in cfg.lr_steps] values = [cfg.learning_rate*(cfg.lr_gama**i) for i in range(len(cfg.lr_steps)+1)] lr = tf.train.piecewise_constant(step, boundaries, values) @@ -57,7 +57,7 @@ def train_SRFBN(dataset, sess, cfg): tf.global_variables_initializer().run(session=sess) summary_writer = tf.summary.FileWriter(cfg.srfbn_logdir, srfbn.sess.graph) - #加载模型 + #load model if srfbn.cfg.load_premodel: counter = srfbn.load() else: @@ -67,10 +67,10 @@ def train_SRFBN(dataset, sess, cfg): global_step = 0 for ep in range(cfg.epoch): - #每次训练时挑选照片的顺序是随机的 + #pick pic randomly pic_idx = np.random.permutation(len(dataset)) picid = 0 - #一次加载五张图像 + #load five pics one time for i in range(0,len(dataset),5): index = [] for j in range(5): @@ -82,7 +82,7 @@ def train_SRFBN(dataset, sess, cfg): print(imgnames) batch_labels, batch_images = preprocess(imgnames, cfg) patch_idx = list(range(len(batch_labels))) - #使得图片块的数量刚好能被batchsize整除 + #make the number of pic chunk divided by batchsize if len(patch_idx) % cfg.batchsize != 0: patch_idx.extend(list(np.random.choice(patch_idx, cfg.batchsize * ((len(patch_idx) // cfg.batchsize)+1) - len(patch_idx)))) @@ -126,7 +126,7 @@ def train_SRFBN(dataset, sess, cfg): global_step += 1 counter += 1 -#训练 +#train def train(*args, **kwargs): data_dir = kwargs["data_dir"] imgs = [os.path.join(data_dir,data) for data in os.listdir(data_dir)] -- Gitee From 89fb08fcabe32ab716f751af4b9b18ad91b5b7f0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 17:05:08 +0000 Subject: [PATCH 21/26] =?UTF-8?q?=E6=8F=90=E4=BA=A4=E5=8D=95=E5=8D=A1?= =?UTF-8?q?=E5=85=A8=E9=87=8F=E8=AE=AD=E7=BB=83=E8=84=9A=E6=9C=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../test/train_full_1p.sh | 184 ++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_full_1p.sh diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_full_1p.sh new file mode 100644 index 000000000..1242fb044 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_full_1p.sh @@ -0,0 +1,184 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo "usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=`cat /etc/passwd |grep ma-user` +if [ x"${modelarts_flag}" != x ]; +then + echo "running with modelarts_flag..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +if [ x"${modelarts_flag}" != x ]; +then + python3 ./train.py +else + python3.7 ./train.py --data_path=${data_path} --output_path=${output_path} 1>${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`((cat ${print_log} | grep "time" | head -n 1) && (cat ${print_log} | grep "time" | tail -n 1)) | awk -F ':' '{print $5 $6 }' | awk -F ',' '{print $1 $2}' | awk -F ' ' '{print $1;print $3}' | awk '{if (NR == 1){a=$1} else if (NR == 2){b=$1} else if (NR == 3){c=$1} else if (NR == 4){d=$1}} END {print (d-b)/(c-a)}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` +#PSNR值计算 +PSNR=`cat ${print_log} | grep "time" | tail -n 10 | awk -F ',' '{print $8}' | awk -F ':' '{sum+=$2} END {print sum/NR}'` +# 提取所有loss打印信息 +grep "loss:" ${print_log} | awk -F "," '{print $6}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +tf_flag=`echo ${Network} | grep TensorFlow | wc -l` +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 -a x"${tf_flag}" == x1 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" +echo "PSNR : $PSNR" +# 输出训练精度 +#echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -- Gitee From 658e31da64084342affe906a73daeb0d39fd5d28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 17:07:29 +0000 Subject: [PATCH 22/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh index 5882b5f4f..708a9d41c 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test/train_performance_1p.sh @@ -100,8 +100,7 @@ start_time=$(date +%s) train_epochs=1 batch_size=1 print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -python3 ./train.py > ${print_log} -# 性能相关数据计算 +python3.7 ./train.py --data_path=${data_path} --output_path=${output_path} 1>${print_log} 2>&1 StepTime=`((cat ${print_log} | grep "time" | head -n 1) && (cat ${print_log} | grep "time" | tail -n 1)) | awk -F ':' '{print $5 $6 }' | awk -F ',' '{print $1 $2}' | awk -F ' ' '{print $1;print $3}' | awk '{if (NR == 1){a=$1} else if (NR == 2){b=$1} else if (NR == 3){c=$1} else if (NR == 4){d=$1}} END {print (d-b)/(c-a)}'` FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` #PSNR值计算 -- Gitee From 17f28971c5f881303051330cd3c39416d014d470 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 17:09:31 +0000 Subject: [PATCH 23/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py index 66419630c..4d1161511 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py @@ -56,7 +56,7 @@ def test_SRFBN(image_lr,image_hr): srfbn.outs] srfbn.losses = tf.reduce_sum(srfbn.losses) / len(srfbn.losses) / srfbn.cfg.batchsize + srfbn.l2_regularization_loss load_flag += 1 - cv2.namedWindow("result", 0) + #cv2.namedWindow("result", 0) img_hr = image_hr.reshape([1,height*srfbn.cfg.scale,width*srfbn.cfg.scale,3]) img_lr = image_lr.reshape([1, height, width, 3]) @@ -66,8 +66,8 @@ def test_SRFBN(image_lr,image_hr): PSNR = compare_psnr(output, img_hr, data_range=255) ssim = _structural_similarity.structural_similarity(output, img_hr,win_size=11, data_range=255, multichannel=True) print("loss:[%.8f], l2_loss:[%.8f], PSNR:[%.8f], SSIM:[%.8f]"%(err,l2_loss,PSNR,ssim)) - cv2.imshow("result", np.uint8(output)) - cv2.waitKey(0) + #cv2.imshow("result", np.uint8(output)) + #cv2.waitKey(0) -- Gitee From b0997c0cffbd8cadcfe2ef5262bca033c65b5c3a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Sun, 6 Nov 2022 17:15:24 +0000 Subject: [PATCH 24/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py | 1 - 1 file changed, 1 deletion(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py index 4d1161511..be8f5570f 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/test.py @@ -41,7 +41,6 @@ def test_SRFBN(image_lr,image_hr): #image height, width, _ = image_lr.shape - print(height,width) global load_flag global srfbn global out -- Gitee From 1eac013c1a195ab78d931c9c45c5eba19992c8fe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Mon, 7 Nov 2022 03:45:01 +0000 Subject: [PATCH 25/26] update TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md index c322a7d11..edf8fc968 100644 --- a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/README.md @@ -23,7 +23,7 @@ SRFBN是采取反馈连接来提高重建超分辨率图片效果的网络模型 ``` - 参考论文： - https://arxiv.org/abs/1903.09814v2 + https://arxiv.org/pdf/1903.09814.pdf - 参考实现： -- Gitee From eb9628e7edd96dc6f368adc5d7e4293ae3e99023 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=81=82=E4=BD=B3=E5=A8=81?= <2929019588@qq.com> Date: Mon, 7 Nov 2022 04:52:50 +0000 Subject: [PATCH 26/26] =?UTF-8?q?=E8=AE=AD=E7=BB=83=E7=8A=B6=E6=80=81?= =?UTF-8?q?=E8=AF=B4=E6=98=8E?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 聂佳威 <2929019588@qq.com> --- .../contrib/cv/SRFBN_for_TensorFlow/modelzoo_level.txt | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 TensorFlow/contrib/cv/SRFBN_for_TensorFlow/modelzoo_level.txt diff --git a/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/modelzoo_level.txt b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/modelzoo_level.txt new file mode 100644 index 000000000..9981888d4 --- /dev/null +++ b/TensorFlow/contrib/cv/SRFBN_for_TensorFlow/modelzoo_level.txt @@ -0,0 +1,6 @@ +GPUStatus:OK +NPUMigrationStatus:OK +FuncStatus:OK +PrecisionStatus:POK +AutoTune:OK +PerfStatus:PERFECT \ No newline at end of file -- Gitee