先上总结

主要是由于代码是基于tensorflow1.0编写的，目前最新的版本是2.0，版本更新比较大，很多方法已经改名，或者弃用。多数报错为 has no attribute 'xxx'

为确保高版本的TF支持低版本的TF代码，升级脚本加入了 compat.v1 模块。

此模块将以等效的 tf.compat.v1.foo 引用代替表单 tf.foo 的调用。

不过，建议您手动检查此类替代方案，并尽快将其迁移至 tf.* 命名空间（代替 tf.compat.v1.* 命名空间）中的新 API。

还有一些其他模块，新的版本中方法名有所变动

问题1 compare_ssim找不到

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/util.py", line 5, in <module>

from skimage.measure import compare_ssim as ssim

模块已经改名

修改/home/mist/1DeepAdverserialRegulariser/ClassFiles/util.py

改为

from skimage.metrics import structural_similarity as ssim

问题2 cannot import name 'imresize'

Traceback (most recent call last):

File "Adversarial_Regulariser.py", line 3, in <module>

from ClassFiles.data_pips import LUNA

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/data_pips.py", line 7, in <module>

from scipy.misc import imresize

ImportError: cannot import name 'imresize' from 'scipy.misc' (/home/mist/anaconda3/lib/python3.8/site-packages/scipy/misc/__init__.py)

imresize已经弃用。使用PIL中的方法代替：

from scipy.misc import imresize

改为

from PIL import Image

def reshape_pic(self, pic):

pic = ut.normalize_image(pic)

pic = imresize(pic, [128, 128]) 这行要修改

pic = ut.scale_to_unit_intervall(pic)

return pic

pic = imresize(pic, [128, 128])

改为

pic = np.array(Image.fromarray(pic).resize((128,128)))

问题3 has no attribute 'InteractiveSession'

Training Data found: 0

Evaluation Data found: 0

/home/mist/anaconda3/lib/python3.8/site-packages/odl/tomo/backends/skimage_radon.py:144: FutureWarning: 'filter' is a deprecated argument name for `iradon`. It will be removed in version 0.19. Please use 'filter_name' instead.

backproj = iradon(skimage_sinogram.asarray().T, theta,

Traceback (most recent call last):

File "Adversarial_Regulariser.py", line 37, in <module>

experiment = Experiment1(DATA_PATH, SAVES_PATH)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/Framework.py", line 120, in __init__

super(AdversarialRegulariser, self).__init__(data_path, saves_path)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/Framework.py", line 45, in __init__

self.sess = tf.InteractiveSession()

AttributeError: module 'tensorflow' has no attribute 'InteractiveSession'

从compat.v1 模块引入

self.sess = tf.InteractiveSession()

改为

self.sess = tf.compat.v1.InteractiveSession()

问题4 has no attribute 'placeholder'

2021-09-15 19:29:35.788948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9649 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:03:00.0, compute capability: 7.5

Traceback (most recent call last):

File "Adversarial_Regulariser.py", line 37, in <module>

experiment = Experiment1(DATA_PATH, SAVES_PATH)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/Framework.py", line 127, in __init__

self.gen_im = tf.placeholder(shape=[None, self.image_space[0], self.image_space[1], self.colors],

AttributeError: module 'tensorflow' has no attribute 'placeholder'

从compat.v1 模块引入

import tensorflow as tf

改为

import tensorflow.compat.v1 as tf

tf.disable_v2_behavior()

问题 5 has no attribute 'layers'

2021-09-15 19:34:42.613666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9649 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:03:00.0, compute capability: 7.5

Traceback (most recent call last):

File "Adversarial_Regulariser.py", line 37, in <module>

experiment = Experiment1(DATA_PATH, SAVES_PATH)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/Framework.py", line 137, in __init__

self.gen_was = self.network.net(self.gen_im)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/networks.py", line 91, in net

conv1 = tf.layers.conv2d(inputs=input, filters=16, kernel_size=[5, 5], padding="same",

AttributeError: module 'tensorflow' has no attribute 'layers'

从compat.v1 模块引入

修改

class ConvNetClassifier(network):

# classical classifier with convolutional layers with strided convolutions and two dense layers at the end

def net(self, input):

# convolutional network for feature extraction

tf.layers = tf.compat.v1.layers 增加这行，猴子补丁

conv1 = tf.layers.conv2d(inputs=input, filters=16, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv1')

conv2 = tf.layers.conv2d(inputs=conv1, filters=32, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv2')

conv3 = tf.layers.conv2d(inputs=conv2, filters=32, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv3', strides=2)

# image size is now size/2

conv4 = tf.layers.conv2d(inputs=conv3, filters=64, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv4', strides=2)

# image size is now size/4

conv5 = tf.layers.conv2d(inputs=conv4, filters=64, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv5', strides=2)

# image size is now size/8

conv6 = tf.layers.conv2d(inputs=conv5, filters=128, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv6', strides=2)

问题6 has no attribute 'AUTO_REUSE'

module 'tensorflow' has no attribute 'AUTO_REUSE'

从compat.v1 模块引入

class ConvNetClassifier(network):

# classical classifier with convolutional layers with strided convolutions and two dense layers at the end

def net(self, input):

# convolutional network for feature extraction

tf.layers = tf.compat.v1.layers

tf.AUTO_REUSE = tf.compat.v1.AUTO_REUSE 增加这行。新增猴子补丁

conv1 = tf.layers.conv2d(inputs=input, filters=16, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv1')

conv2 = tf.layers.conv2d(inputs=conv1, filters=32, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv2')

conv3 = tf.layers.conv2d(inputs=conv2, filters=32, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv3', strides=2)

# image size is now size/2

conv4 = tf.layers.conv2d(inputs=conv3, filters=64, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv4', strides=2)

# image size is now size/4

conv5 = tf.layers.conv2d(inputs=conv4, filters=64, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv5', strides=2)

# image size is now size/8

conv6 = tf.layers.conv2d(inputs=conv5, filters=128, kernel_size=[5, 5], padding="same",

activation=lrelu, reuse=tf.AUTO_REUSE, name='conv6', strides=2)

问题7 has no attribute 'get_default_graph'

Traceback (most recent call last):

File "Adversarial_Regulariser.py", line 37, in <module>

experiment = Experiment1(DATA_PATH, SAVES_PATH)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/Framework.py", line 174, in __init__

self.ray = self.model.tensorflow_operator(self.reconstruction)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/forward_models.py", line 103, in tensorflow_operator

return self.ray_transform(tensor)

File "/home/mist/anaconda3/lib/python3.8/site-packages/odl/contrib/tensorflow/layer.py", line 369, in tensorflow_layer

result = py_func(_impl,

File "/home/mist/anaconda3/lib/python3.8/site-packages/odl/contrib/tensorflow/layer.py", line 99, in py_func

g = tf.get_default_graph()

AttributeError: module 'tensorflow' has no attribute 'get_default_graph'

修改源码文件：/home/mist/anaconda3/lib/python3.8/site-packages/odl/contrib/tensorflow/layer.py

在前面增加这行：tf.get_default_graph = tf.compat.v1.get_default_graph

猴子补丁：

如图下所示：

问题8 has no attribute 'py_func'

Instructions for updating:

Call initializer instance with the dtype argument instead of passing it to the constructor

Traceback (most recent call last):

File "Adversarial_Regulariser.py", line 37, in <module>

experiment = Experiment1(DATA_PATH, SAVES_PATH)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/Framework.py", line 174, in __init__

self.ray = self.model.tensorflow_operator(self.reconstruction)

File "/home/mist/1DeepAdverserialRegulariser/ClassFiles/forward_models.py", line 103, in tensorflow_operator

return self.ray_transform(tensor)

File "/home/mist/anaconda3/lib/python3.8/site-packages/odl/contrib/tensorflow/layer.py", line 371, in tensorflow_layer

result = py_func(_impl,

File "/home/mist/anaconda3/lib/python3.8/site-packages/odl/contrib/tensorflow/layer.py", line 104, in py_func

return tf.py_func(func, inp, Tout, stateful=stateful,

AttributeError: module 'tensorflow' has no attribute 'py_func'

修改源码文件：/home/mist/anaconda3/lib/python3.8/site-packages/odl/contrib/tensorflow/layer.py

在前面增加这行：tf.py_func=tf.compat.v1.py_func

如图下所示

astra-toolbox 模块安装

使用conda命令安装即可

conda install -c astra-toolbox/label/dev astra-toolbox

安装详情打印如下：

$ conda install -c astra-toolbox/label/dev astra-toolbox
Collecting package metadata (current_repodata.json): /
done
Solving environment: -
The environment is inconsistent, please check the package plan carefully
The following packages are causing the inconsistency:- defaults/noarch::seaborn==0.11.1=pyhd3eb1b0_0- defaults/linux-64::scipy==1.6.2=py38had2a1c9_1- defaults/linux-64::anaconda==2021.05=py38_0- defaults/linux-64::pywavelets==1.1.1=py38h7b6447c_2- defaults/linux-64::patsy==0.5.1=py38_0- defaults/linux-64::pandas==1.2.4=py38h2531618_0- defaults/linux-64::statsmodels==0.12.2=py38h27cfd23_0- defaults/linux-64::numba==0.53.1=py38ha9443f7_0- defaults/linux-64::pyerfa==1.7.3=py38h27cfd23_0- defaults/linux-64::bottleneck==1.3.2=py38heb32a55_1- defaults/linux-64::pytables==3.6.1=py38h9fd0a39_0- defaults/linux-64::astropy==4.2.1=py38h27cfd23_1- defaults/linux-64::mkl_fft==1.3.0=py38h42c9631_2- defaults/linux-64::tifffile==2020.10.1=py38hdd07704_2- defaults/linux-64::matplotlib==3.3.4=py38h06a4308_0- defaults/linux-64::scikit-learn==0.24.1=py38ha9443f7_0- defaults/linux-64::matplotlib-base==3.3.4=py38h62a2d02_0- defaults/linux-64::bokeh==2.3.2=py38h06a4308_0- defaults/linux-64::bkcharts==0.2=py38_0- defaults/linux-64::mkl_random==1.2.1=py38ha9443f7_2- defaults/noarch::imageio==2.9.0=pyhd3eb1b0_0- defaults/noarch::dask==2021.4.0=pyhd3eb1b0_0- defaults/linux-64::numexpr==2.7.3=py38h22e1b3c_1- defaults/linux-64::numpy==1.20.1=py38h93e21f0_0- defaults/linux-64::scikit-image==0.18.1=py38ha9443f7_0
done==> WARNING: A newer version of conda exists. <==current version: 4.10.1latest version: 4.10.3Please update conda by running$ conda update -n base -c defaults conda## Package Plan ##environment location: /home/mist/anaconda3added / updated specs:- astra-toolboxThe following packages will be downloaded:package                    |            build---------------------------|-----------------_anaconda_depends-2020.07  |           py38_0           6 KB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/mainanaconda-custom            |           py38_1          35 KB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/mainastra-toolbox-1.9.9.dev7   |   py38h9d24096_0         432 KB  astra-toolbox/label/devcudatoolkit-10.2.89        |       hfd86e86_1       365.1 MB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/mainh5py-2.10.0                |   py38h7918eee_0         1.1 MB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/mainlibllvm9-9.0.1             |       he513fc3_1        25.1 MB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forgenumpy-base-1.20.1          |   py38h7d8b39e_0         4.6 MB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/mainopenssl-1.1.1l             |       h7f8727e_0         2.5 MB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/mainpathtools-0.1.2            |             py_1           8 KB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forgesnappy-1.1.8               |       he1b5a44_3          32 KB  https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge------------------------------------------------------------Total:       398.8 MBThe following NEW packages will be INSTALLED:_anaconda_depends  anaconda/pkgs/main/linux-64::_anaconda_depends-2020.07-py38_0astra-toolbox      astra-toolbox/label/dev/linux-64::astra-toolbox-1.9.9.dev7-py38h9d24096_0cudatoolkit        anaconda/pkgs/main/linux-64::cudatoolkit-10.2.89-hfd86e86_1h5py               anaconda/pkgs/main/linux-64::h5py-2.10.0-py38h7918eee_0libastra           astra-toolbox/label/dev/linux-64::libastra-1.9.9.dev7-cuda_10.2_0libllvm9           anaconda/cloud/conda-forge/linux-64::libllvm9-9.0.1-he513fc3_1numpy-base         anaconda/pkgs/main/linux-64::numpy-base-1.20.1-py38h7d8b39e_0pathtools          anaconda/cloud/conda-forge/noarch::pathtools-0.1.2-py_1python_abi         anaconda/cloud/conda-forge/linux-64::python_abi-3.8-2_cp38snappy             anaconda/cloud/conda-forge/linux-64::snappy-1.1.8-he1b5a44_3The following packages will be UPDATED:ca-certificates    pkgs/main::ca-certificates-2021.4.13-~ --> anaconda/cloud/conda-forge::ca-certificates-2021.5.30-ha878542_0certifi            pkgs/main::certifi-2020.12.5-py38h06a~ --> anaconda/cloud/conda-forge::certifi-2021.5.30-py38h578d9bd_0conda              pkgs/main::conda-4.10.1-py38h06a4308_1 --> anaconda/cloud/conda-forge::conda-4.10.3-py38h578d9bd_1openssl              pkgs/main::openssl-1.1.1k-h27cfd23_0 --> anaconda/pkgs/main::openssl-1.1.1l-h7f8727e_0The following packages will be SUPERSEDED by a higher-priority channel:anaconda               pkgs/main::anaconda-2021.05-py38_0 --> anaconda/pkgs/main::anaconda-custom-py38_1Proceed ([y]/n)?Downloading and Extracting Packages
libllvm9-9.0.1       | 25.1 MB   | ################################################################################################################################################################################################################################### | 100%
astra-toolbox-1.9.9. | 432 KB    | ################################################################################################################################################################################################################################### | 100%
snappy-1.1.8         | 32 KB     | ################################################################################################################################################################################################################################### | 100%
cudatoolkit-10.2.89  | 365.1 MB  | ################################################################################################################################################################################################################################### | 100%
openssl-1.1.1l       | 2.5 MB    | ################################################################################################################################################################################################################################### | 100%
anaconda-custom      | 35 KB     | ################################################################################################################################################################################################################################### | 100%
h5py-2.10.0          | 1.1 MB    | ################################################################################################################################################################################################################################### | 100%
numpy-base-1.20.1    | 4.6 MB    | ################################################################################################################################################################################################################################### | 100%
_anaconda_depends-20 | 6 KB      | ################################################################################################################################################################################################################################### | 100%
pathtools-0.1.2      | 8 KB      | ################################################################################################################################################################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(base)

安装完毕后

pip list |grep toolbox 命令可以查到安装的包

Anaconda3环境使用TensorFlow报错解决记录 mistGPU相关推荐

kotlin Unresolved reference报错解决记录
遇到一个抓狂的问题就是项目转kotlin后一直报:Unresolved reference:XXXXXXX 项目预编译可以没报错,但一mark就报这个问题,问题所报位置都和项目中使用kotlin定义的 ...
ajaxSubmit报错解决记录
ajaxSubmit提交js报错,需要在页面单独引入jquery.form.js <scripttype="text/JavaScript" src="${page ...
Python 虚拟环境迁移路径后pip报错解决记录
为了保持不同项目之间包管理的独立,一般每个项目中都会独用一个虚拟环境venv,venv放在项目根目录下,在项目迁移的时候虚拟环境一般是不敢乱动的,容易报很多莫名其妙的错误,比如:因此,每次移动项目都是 ...
keras和tensorflow 报错解决：UserWarning: Method on_batch_end() is slow compared to the batch update Check
早上在使用keras时报错如下: C:\Users\peter\Anaconda3\lib\site-packages\keras\callbacks.py:122: UserWarning: Met ...
python3.7.4怎么运行_记一次win7在python3.7.4环境启动ride报错解决
配置: 系统:windows win7 python版本:3.7.4 robotframework-ride版本:1.7.4.1 问题: 双击快捷方式.双击ride.py文件都打不开ride界面命令 ...
小程序app.json: app.json 未找到报错解决记录
在调试腾讯云的产品过程中遇到了这个问题,查了一通发现很多所谓的解决方法都不在根本上,特此记录下首先,小程序必须要有app.json app.js app.wxss这三个主体,并且要放在根目录中, 在 ...
IDEA Maven 多module项目导入 pom文件报错解决记录
具体描述: Non-resolvable parent POM: Could not find artifact 具体例子: root下的pom文件 <?xml ...
Python3.5 win10环境下导入kera/tensorflow报错的解决方法
这篇文章主要介绍了Python3.5 win10环境下导入keras/tensorflow报错的解决方法,较为详细的分析了Python3.5在win10环境下导入keras/tensorflow提示错 ...
解决tensorflow报错：AttributeError: module ‘tensorflow.keras.backend‘ has no attribute ‘get_session‘ 问题
欢迎大家关注笔者,你的关注是我持续更博的最大动力原创文章,转载告知,盗版必究解决tensorflow报错:AttributeError: module 'tensorflow.keras.back ...

Anaconda3环境使用TensorFlow报错解决记录 mistGPU

先上总结

问题1 compare_ssim找不到

问题2 cannot import name 'imresize'

问题3 has no attribute 'InteractiveSession'

问题4 has no attribute 'placeholder'

问题 5 has no attribute 'layers'

问题6 has no attribute 'AUTO_REUSE'

问题7 has no attribute 'get_default_graph'

问题8 has no attribute 'py_func'

astra-toolbox 模块安装

Anaconda3环境使用TensorFlow报错解决记录 mistGPU相关推荐

最新文章

热门文章