taehoonlee / tensornets Goto Github PK
View Code? Open in Web Editor NEWHigh level network definitions with pre-trained weights in TensorFlow
License: MIT License
High level network definitions with pre-trained weights in TensorFlow
License: MIT License
Recently, I have updated the performance table to follow the recent report format. I reviewed 10 recent papers as follows, and found that error rates and 10-crop testing are no longer used for evaluation after about 2017.
measure | 10-crop | |
---|---|---|
ResNeXt (CVPR 2017) | err | O |
DenseNet (CVPR 2017) | err | O |
PolyNet (CVPR 2017) | err | O |
DPN (NIPS 2017) | err | - |
SENet (CVPR 2018) | err | - |
MobileNetV2 (CVPR 2018) | acc | - |
NASNet (CVPR 2018) | acc | - |
PNASNet (ECCV 2018) | acc | - |
AmoebaNet (AAAI 2019) | acc | - |
EfficientNet (ICML 2019) | acc | - |
For the above reason, the recent commit replaces the top-k error rates with accuracies and removes the 10-5 column (the top-5 score with 10 crops).
I use the code like below,
...
resnet = partial(tensornets.ResNet50, reuse=tf.AUTO_REUSE)
outputs_1 = resnet(x_1).get_outputs()
outputs_2 = resnet(x_2).get_outputs()
...
outputs_2
appends the outputs of x_2
to outputs_1
.
When I installed TensorNets from PyPI (pip install tensornets) , what happened as:
Building wheels for collected packages: tensornets
Running setup.py bdist_wheel for tensornets ... error
Complete output from command e:\python\anaconda_setup\python.exe -u -c "import setuptools, tokenize;file='C:\Users\hp\AppData\Local\Temp\pip-install-u8ygwt_s\tensornets\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" bdist_wheel -d C:\Users\hp\AppData\Local\Temp\pip-wheel-9cd_yi__ --python-tag cp36:
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.6
creating build\lib.win-amd64-3.6\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\unet.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\wavenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets_init_.py -> build\lib.win-amd64-3.6\tensornets
creating build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\coco.py -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets_init_.py -> build\lib.win-amd64-3.6\tensornets\datasets
creating build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references_init_.py -> build\lib.win-amd64-3.6\tensornets\references
creating build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils_init_.py -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
running egg_info
writing tensornets.egg-info\PKG-INFO
writing dependency_links to tensornets.egg-info\dependency_links.txt
writing top-level names to tensornets.egg-info\top_level.txt
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\datasets\coco.names -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\darkflow_utils_init_.pyc -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.pyc -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.so -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.so -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
creating build\temp.win-amd64-3.6
creating build\temp.win-amd64-3.6\Release
creating build\temp.win-amd64-3.6\Release\tensornets
creating build\temp.win-amd64-3.6\Release\tensornets\references
creating build\temp.win-amd64-3.6\Release\tensornets\references\darkflow_utils
D:\MSVSC\VC\Tools\MSVC\14.15.26726\bin\HostX86\x64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ie:\python\anaconda_setup\lib\site-packages\numpy\core\include -Ie:\python\anaconda_setup\include -Ie:\python\anaconda_setup\include -ID:\MSVSC\VC\Tools\MSVC\14.15.26726\ATLMFC\include -ID:\MSVSC\VC\Tools\MSVC\14.15.26726\include "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\shared" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\winrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\cppwinrt" /Tctensornets/references/darkflow_utils/nms.c /Fobuild\temp.win-amd64-3.6\Release\tensornets/references/darkflow_utils/nms.obj
nms.c
e:\python\anaconda_setup\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
tensornets/references/darkflow_utils/nms.c(2437): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(2446): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(2496): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(22776): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(22782): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
D:\MSVSC\VC\Tools\MSVC\14.15.26726\bin\HostX86\x64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:e:\python\anaconda_setup\libs /LIBPATH:e:\python\anaconda_setup\PCbuild\amd64 /LIBPATH:D:\MSVSC\VC\Tools\MSVC\14.15.26726\ATLMFC\lib\x64 /LIBPATH:D:\MSVSC\VC\Tools\MSVC\14.15.26726\lib\x64 "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.17134.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.17134.0\um\x64" m.lib /EXPORT:PyInit_nms build\temp.win-amd64-3.6\Release\tensornets/references/darkflow_utils/nms.obj /OUT:build\lib.win-amd64-3.6\tensornets\references\darkflow_utils\nms.cp36-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.6\Release\tensornets/references/darkflow_utils\nms.cp36-win_amd64.lib
LINK : fatal error LNK1181: 无法打开输入文件“m.lib”
error: command 'D:\MSVSC\VC\Tools\MSVC\14.15.26726\bin\HostX86\x64\link.exe' failed with exit status 1181
Failed building wheel for tensornets
Running setup.py clean for tensornets
Failed to build tensornets
Installing collected packages: tensornets
Running setup.py install for tensornets ... error
Complete output from command e:\python\anaconda_setup\python.exe -u -c "import setuptools, tokenize;file='C:\Users\hp\AppData\Local\Temp\pip-install-u8ygwt_s\tensornets\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record C:\Users\hp\AppData\Local\Temp\pip-record-al4vrtuo\install-record.txt --single-version-externally-managed --compile:
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.6
creating build\lib.win-amd64-3.6\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\unet.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\wavenets.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.6\tensornets
copying tensornets_init_.py -> build\lib.win-amd64-3.6\tensornets
creating build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\coco.py -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets_init_.py -> build\lib.win-amd64-3.6\tensornets\datasets
creating build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references_init_.py -> build\lib.win-amd64-3.6\tensornets\references
creating build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils_init_.py -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
running egg_info
writing tensornets.egg-info\PKG-INFO
writing dependency_links to tensornets.egg-info\dependency_links.txt
writing top-level names to tensornets.egg-info\top_level.txt
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\datasets\coco.names -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.6\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.6\tensornets\references
copying tensornets\references\darkflow_utils_init_.pyc -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.pyc -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.so -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.so -> build\lib.win-amd64-3.6\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
creating build\temp.win-amd64-3.6
creating build\temp.win-amd64-3.6\Release
creating build\temp.win-amd64-3.6\Release\tensornets
creating build\temp.win-amd64-3.6\Release\tensornets\references
creating build\temp.win-amd64-3.6\Release\tensornets\references\darkflow_utils
D:\MSVSC\VC\Tools\MSVC\14.15.26726\bin\HostX86\x64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ie:\python\anaconda_setup\lib\site-packages\numpy\core\include -Ie:\python\anaconda_setup\include -Ie:\python\anaconda_setup\include -ID:\MSVSC\VC\Tools\MSVC\14.15.26726\ATLMFC\include -ID:\MSVSC\VC\Tools\MSVC\14.15.26726\include "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\shared" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\winrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\cppwinrt" /Tctensornets/references/darkflow_utils/nms.c /Fobuild\temp.win-amd64-3.6\Release\tensornets/references/darkflow_utils/nms.obj
nms.c
e:\python\anaconda_setup\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
tensornets/references/darkflow_utils/nms.c(2437): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(2446): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(2496): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(22776): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
tensornets/references/darkflow_utils/nms.c(22782): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
D:\MSVSC\VC\Tools\MSVC\14.15.26726\bin\HostX86\x64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:e:\python\anaconda_setup\libs /LIBPATH:e:\python\anaconda_setup\PCbuild\amd64 /LIBPATH:D:\MSVSC\VC\Tools\MSVC\14.15.26726\ATLMFC\lib\x64 /LIBPATH:D:\MSVSC\VC\Tools\MSVC\14.15.26726\lib\x64 "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.17134.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.17134.0\um\x64" m.lib /EXPORT:PyInit_nms build\temp.win-amd64-3.6\Release\tensornets/references/darkflow_utils/nms.obj /OUT:build\lib.win-amd64-3.6\tensornets\references\darkflow_utils\nms.cp36-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.6\Release\tensornets/references/darkflow_utils\nms.cp36-win_amd64.lib
LINK : fatal error LNK1181: 无法打开输入文件“m.lib”
error: command 'D:\MSVSC\VC\Tools\MSVC\14.15.26726\bin\HostX86\x64\link.exe' failed with exit status 1181
----------------------------------------
Command "e:\python\anaconda_setup\python.exe -u -c "import setuptools, tokenize;file='C:\Users\hp\AppData\Local\Temp\pip-install-u8ygwt_s\tensornets\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record C:\Users\hp\AppData\Local\Temp\pip-record-al4vrtuo\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in C:\Users\hp\AppData\Local\Temp\pip-install-u8ygwt_s\tensornets\
My system is windows10 and my python is 3.6, tensorflow is 1.12.0 which is based on cpu.
Can anyone help me? Please!
my code can run without any error in one device. when i copy the code to the another device, it crashed.
here is the error:
File "/media/profyuan/新加卷2/JD_Fashion_AI/JD_Fashion_AI_original/train.py", line 204, in
main()
File "/media/profyuan/新加卷2/JD_Fashion_AI/JD_Fashion_AI_original/train.py", line 200, in main
run_training()
File "/media/profyuan/新加卷2/JD_Fashion_AI/JD_Fashion_AI_original/train.py", line 152, in run_training
sess.run(model.pretrained())
File "/usr/local/lib/python2.7/dist-packages/tensornets/pretrained.py", line 94, in _direct
return fun(scope, return_fn=pretrained_initializer)
File "/usr/local/lib/python2.7/dist-packages/tensornets/pretrained.py", line 186, in load_resnet101
return return_fn(scopes, values)
File "/usr/local/lib/python2.7/dist-packages/tensornets/utils.py", line 292, in pretrained_initializer
assert len(weights) == len(values), 'The sizes of symbolic and '
AssertionError: The sizes of symbolic and actual weights do not match.
here is my pip list:
absl-py 0.2.1
adium-theme-ubuntu 0.3.4
astor 0.6.2
backports-abc 0.5
backports.shutil-get-terminal-size 1.0.0
backports.weakref 1.0.post1
bleach 1.5.0
configparser 3.5.0
cycler 0.9.0
Cython 0.28.2
decorator 4.0.6
easydict 1.7
entrypoints 0.2.3
enum34 1.1.6
funcsigs 1.0.2
functools32 3.2.3.post2
futures 3.2.0
gast 0.2.0
grpcio 1.12.0
h5py 2.7.1
html5lib 0.9999999
ipykernel 4.8.2
ipython 5.7.0
ipython-genutils 0.2.0
ipywidgets 7.2.1
Jinja2 2.10
jsonschema 2.6.0
jupyter 1.0.0
jupyter-client 5.2.3
jupyter-console 5.2.0
jupyter-core 4.4.0
Keras 2.1.6
Markdown 2.6.11
MarkupSafe 1.0
matplotlib 1.5.1
mistune 0.8.3
mock 2.0.0
nbconvert 5.3.1
nbformat 4.4.0
nose 1.3.7
notebook 5.5.0
numpy 1.14.3
opencv-python 3.4.0.12
pandocfilters 1.4.2
pathlib2 2.3.2
pbr 4.0.2
pexpect 4.5.0
pickleshare 0.7.4
Pillow 5.1.0
pip 10.0.1
prompt-toolkit 1.0.15
protobuf 3.5.2.post1
ptyprocess 0.5.2
Pygments 2.2.0
pyparsing 2.0.3
Pyste 0.9.10
python-dateutil 2.4.2
pytz 2014.10
PyYAML 3.12
pyzmq 17.0.0
qtconsole 4.3.1
scandir 1.7
scikit-image 0.10.1
scikit-learn 0.19.1
scipy 0.17.0
Send2Trash 1.5.0
setuptools 20.7.0
simplegeneric 0.8.1
singledispatch 3.4.0.3
six 1.10.0
sklearn 0.0
tensorboard 1.8.0
tensorflow 1.8.0
tensorflow-gpu 1.4.0
tensorflow-tensorboard 0.4.0
tensornets 0.3.3
termcolor 1.1.0
terminado 0.8.1
testpath 0.3.1
tornado 5.0.2
traitlets 4.3.2
unity-lens-photos 1.0
virtualenv 15.0.1
wcwidth 0.1.7
Werkzeug 0.14.1
wheel 0.29.0
widgetsnbextension 3.2.1
pip install pybluez
Collecting pybluez
Using cached https://files.pythonhosted.org/packages/c1/98/3149481d508bee174335be6725880f00d297afebe75c15e917af8f6fe169/PyBluez-0.22.zip
Complete output from command python setup.py egg_info:
Could not find the Windows Platform SDK
----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in C:\Users\User\AppData\Local\Temp\pip-install-yb8lba9t\pybluez\
ho to solve this question ?
please help me!!!
Command "C:\Users\User\Anaconda2\python.exe -u -c "import setuptools, tokenize;file='c:\users\user\appdata\local\temp\pip-install-jarbs8\pybluez\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record c:\users\user\appdata\local\temp\pip-record-rosdw0\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in c:\users\user\appdata\local\temp\pip-install-jarbs8\pybluez\
Currently, there are only get_outputs()
which returns all the endpoints (too verbose). However, in practice, many users want to access representative endpoints only (e.g., conv5b
in GoogLeNet
).
import tensorflow as tf
import tensornets as nets
inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
model = nets.ResNet50(inputs)
model.get_outputs() # returns a list of length 161
The new API requests are:
model.get_feature() # returns the bottleneck feature
model.get_features() # returns a list of some endpoints
Hello, i having difficulties about install tensornets.
my environment is explaned bellow.
OS: windows 10
Virtual environment: anaconda
Python version: 3.5
tensorflow version: 1.9
PIP version: 18.1
I was activating my tensorflow virtual environment on anaconda then input command like this
pip install tensornets
.
whole prompt return string is here
Collecting tensornets
Using cached https://files.pythonhosted.org/packages/28/77/8f3e6717a3e51d2f26c5d1e6b82599b01a5941dbcc98b39a4dd542502965/tensornets-0.3.5.tar.gz
Building wheels for collected packages: tensornets
Running setup.py bdist_wheel for tensornets ... error
Complete output from command c:\users\bearm\anaconda3\envs\py35tf\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\bearm\\AppData\\Local\\Temp\\pip-install-36vc4dxz\\tensornets\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d C:\Users\bearm\AppData\Local\Temp\pip-wheel-tnqbxh0x --python-tag cp35:
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.5
creating build\lib.win-amd64-3.5\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\unet.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\wavenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\__init__.py -> build\lib.win-amd64-3.5\tensornets
creating build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\coco.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\__init__.py -> build\lib.win-amd64-3.5\tensornets\datasets
creating build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\__init__.py -> build\lib.win-amd64-3.5\tensornets\references
creating build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\__init__.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running egg_info
writing dependency_links to tensornets.egg-info\dependency_links.txt
writing tensornets.egg-info\PKG-INFO
writing top-level names to tensornets.egg-info\top_level.txt
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\datasets\coco.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\darkflow_utils\__init__.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
creating build\temp.win-amd64-3.5
creating build\temp.win-amd64-3.5\Release
creating build\temp.win-amd64-3.5\Release\tensornets
creating build\temp.win-amd64-3.5\Release\tensornets\references
creating build\temp.win-amd64-3.5\Release\tensornets\references\darkflow_utils
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ic:\users\bearm\anaconda3\envs\py35tf\lib\site-packages\numpy\core\include -Ic:\users\bearm\anaconda3\envs\py35tf\include -Ic:\users\bearm\anaconda3\envs\py35tf\include "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\shared" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\winrt" /Tctensornets/references/darkflow_utils/nms.c /Fobuild\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj
nms.c
c:\users\bearm\anaconda3\envs\py35tf\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
tensornets/references/darkflow_utils/nms.c(2437): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2446): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2496): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22776): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22782): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:c:\users\bearm\anaconda3\envs\py35tf\libs /LIBPATH:c:\users\bearm\anaconda3\envs\py35tf\PCbuild\amd64 "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.14393.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.14393.0\um\x64" m.lib /EXPORT:PyInit_nms build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj /OUT:build\lib.win-amd64-3.5\tensornets\references\darkflow_utils\nms.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils\nms.cp35-win_amd64.lib
LINK : fatal error LNK1181: cannot open input file 'm.lib'
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\link.exe' failed with exit status 1181
----------------------------------------
Failed building wheel for tensornets
Running setup.py clean for tensornets
Failed to build tensornets
Installing collected packages: tensornets
Running setup.py install for tensornets ... error
Complete output from command c:\users\bearm\anaconda3\envs\py35tf\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\bearm\\AppData\\Local\\Temp\\pip-install-36vc4dxz\\tensornets\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record C:\Users\bearm\AppData\Local\Temp\pip-record-f7zzi1az\install-record.txt --single-version-externally-managed --compile:
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.5
creating build\lib.win-amd64-3.5\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\unet.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\wavenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\__init__.py -> build\lib.win-amd64-3.5\tensornets
creating build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\coco.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\__init__.py -> build\lib.win-amd64-3.5\tensornets\datasets
creating build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\__init__.py -> build\lib.win-amd64-3.5\tensornets\references
creating build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\__init__.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running egg_info
writing tensornets.egg-info\PKG-INFO
writing dependency_links to tensornets.egg-info\dependency_links.txt
writing top-level names to tensornets.egg-info\top_level.txt
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\datasets\coco.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\darkflow_utils\__init__.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
creating build\temp.win-amd64-3.5
creating build\temp.win-amd64-3.5\Release
creating build\temp.win-amd64-3.5\Release\tensornets
creating build\temp.win-amd64-3.5\Release\tensornets\references
creating build\temp.win-amd64-3.5\Release\tensornets\references\darkflow_utils
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ic:\users\bearm\anaconda3\envs\py35tf\lib\site-packages\numpy\core\include -Ic:\users\bearm\anaconda3\envs\py35tf\include -Ic:\users\bearm\anaconda3\envs\py35tf\include "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\shared" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.14393.0\winrt" /Tctensornets/references/darkflow_utils/nms.c /Fobuild\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj
nms.c
c:\users\bearm\anaconda3\envs\py35tf\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
tensornets/references/darkflow_utils/nms.c(2437): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2446): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2496): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22776): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22782): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:c:\users\bearm\anaconda3\envs\py35tf\libs /LIBPATH:c:\users\bearm\anaconda3\envs\py35tf\PCbuild\amd64 "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\ATLMFC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.14393.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.14393.0\um\x64" m.lib /EXPORT:PyInit_nms build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj /OUT:build\lib.win-amd64-3.5\tensornets\references\darkflow_utils\nms.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils\nms.cp35-win_amd64.lib
LINK : fatal error LNK1181: cannot open input file 'm.lib'
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\link.exe' failed with exit status 1181
----------------------------------------
Command "c:\users\bearm\anaconda3\envs\py35tf\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\bearm\\AppData\\Local\\Temp\\pip-install-36vc4dxz\\tensornets\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record C:\Users\bearm\AppData\Local\Temp\pip-record-f7zzi1az\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in C:\Users\bearm\AppData\Local\Temp\pip-install-36vc4dxz\tensornets\
middle of the return strings, there are link error that
LINK : fatal error LNK1181: cannot open input file 'm.lib'
Is this error message my own environmental error or error in installation code ??
Hello;
I am planning to compare the performance of a few segmentation models and I found the repo when I was looking for the easiest way to implement transfer learning from classifications to segmentation models. The work you have done until now is outstanding and for sure is helping a lot of us.
I am just about to try tensornets but before I would like to ask a couple of questions in order to try to ease my own tasks.
To implement skip connections, to formulate alternative loss functions and in general the usual machinery for segmentation models, I can have access to any level/layer/weight with get_middles()
and get()_outputs
of classification networks, right?
Is easy to integrate tensornets with estimators?
Thank you for you work!!
Hi, I try to install the tensornets on Mint os by pip install tensornets
Here is the ERROR:
`Installing collected packages: tensornets
Running setup.py install for tensornets ... error
Complete output from command /usr/bin/python3 -u -c "import setuptools, tokenize;file='/tmp/pip-install-e0ktv6qy/tensornets/setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record /tmp/pip-record-uv6ejby6/install-record.txt --single-version-externally-managed --compile:
running install
running build
running build_py
creating build
creating build/lib.linux-x86_64-3.5
creating build/lib.linux-x86_64-3.5/tensornets
copying tensornets/zf.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/utils.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/init.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/layers.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/middles.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/inceptions.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/mobilenets.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/detections.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/resnets.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/darknets.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/vggs.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/capsulenets.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/nasnets.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/densenets.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/pretrained.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/squeezenets.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/preprocess.py -> build/lib.linux-x86_64-3.5/tensornets
copying tensornets/ops.py -> build/lib.linux-x86_64-3.5/tensornets
creating build/lib.linux-x86_64-3.5/tensornets/datasets
copying tensornets/datasets/init.py -> build/lib.linux-x86_64-3.5/tensornets/datasets
copying tensornets/datasets/imagenet.py -> build/lib.linux-x86_64-3.5/tensornets/datasets
copying tensornets/datasets/voc.py -> build/lib.linux-x86_64-3.5/tensornets/datasets
creating build/lib.linux-x86_64-3.5/tensornets/references
copying tensornets/references/init.py -> build/lib.linux-x86_64-3.5/tensornets/references
copying tensornets/references/rpn_utils.py -> build/lib.linux-x86_64-3.5/tensornets/references
copying tensornets/references/yolo_utils.py -> build/lib.linux-x86_64-3.5/tensornets/references
copying tensornets/references/rcnns.py -> build/lib.linux-x86_64-3.5/tensornets/references
copying tensornets/references/yolos.py -> build/lib.linux-x86_64-3.5/tensornets/references
creating build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/init.py -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/box.py -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
running egg_info
writing top-level names to tensornets.egg-info/top_level.txt
writing dependency_links to tensornets.egg-info/dependency_links.txt
writing tensornets.egg-info/PKG-INFO
reading manifest file 'tensornets.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info/SOURCES.txt'
copying tensornets/datasets/voc.names -> build/lib.linux-x86_64-3.5/tensornets/datasets
copying tensornets/references/coco.names -> build/lib.linux-x86_64-3.5/tensornets/references
copying tensornets/references/voc.names -> build/lib.linux-x86_64-3.5/tensornets/references
copying tensornets/references/darkflow_utils/init.pyc -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/box.pyc -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/get_boxes.c -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/get_boxes.pyx -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/get_boxes.so -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/nms.c -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/nms.pxd -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/nms.pyx -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
copying tensornets/references/darkflow_utils/nms.so -> build/lib.linux-x86_64-3.5/tensornets/references/darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
creating build/temp.linux-x86_64-3.5
creating build/temp.linux-x86_64-3.5/tensornets
creating build/temp.linux-x86_64-3.5/tensornets/references
creating build/temp.linux-x86_64-3.5/tensornets/references/darkflow_utils
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.5/dist-packages/numpy/core/include -I/usr/include/python3.5m -c tensornets/references/darkflow_utils/nms.c -o build/temp.linux-x86_64-3.5/tensornets/references/darkflow_utils/nms.o
tensornets/references/darkflow_utils/nms.c:26:20: fatal error: Python.h: 没有那个文件或目录
compilation terminated.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 1
----------------------------------------
Command "/usr/bin/python3 -u -c "import setuptools, tokenize;file='/tmp/pip-install-e0ktv6qy/tensornets/setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record /tmp/pip-record-uv6ejby6/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-install-e0ktv6qy/tensornets/
I have installed
tensorflow-gpu==1.8.0`
I wanna test yolo2 or yolov3 combined with mobilenetv2, as following codes, how to download the models?
import tensorflow as tf
import tensornets as nets
inputs = tf.placeholder(tf.float32, [None, 416, 416, 3])
model = nets.YOLOv2(inputs, nets.mobilenets.MobileNet75v2)
img = nets.utils.load_img('cat.jpg')
with tf.Session() as sess:
sess.run(model.pretrained())
preds = sess.run(model, {inputs: model.preprocess(img)})
boxes = model.get_boxes(preds, img.shape[1:3])
print(boxes)
All the models in TensorNets return a softmax tf.Tensor
, while the models in tensorflow/models
(also backbone of tensorflow/hub
) do a logits tf.Tensor
(the values before softmax). That is because most regular TensorFlow loss APIs take a logits as an argument. In results,
tf.losses.softmax_cross_entropy(onehot_labels=outputs, logits=model)
,tf.nn.softmax_cross_entropy_with_logits_v2(labels=outputs, logits=model.logits)
,tf.losses.softmax_cross_entropy(onehot_labels=outputs, logits=model.logits)
,where model
is any model function in TensorNets (e.g., model = nets.MobileNet50(inputs)
) and model.logits
is equivalent to model.get_outputs()[-2]
.
The following is a comparison of the three losses mentioned above and TL; DR.
import numpy as np
import tensorflow as tf
import tensornets as nets
# Define a model
inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
outputs = tf.placeholder(tf.float32, [None, 1000])
model = nets.MobileNet50(inputs)
# `model.logits`: available since TensorNets 0.4.1
logits = model.logits # alternatively: `model.get_outputs()[-2]`
# `model`: the values after softmax
assert 'probs' in model.name
# `logits`: the values before softmax
assert 'logits' in logits.name
# Load feeds
img = nets.utils.load_img('cat.png', target_size=256, crop_size=224)
label = np.zeros((1, 1000)).astype(np.float32)
label[0, 588] = 1.
# Initialize the model
sess = tf.Session()
sess.run(model.pretrained())
# Get results
# 1. invalid usage: `softmax_cross_entropy`
# with predicted softmax values
invalid = tf.losses.softmax_cross_entropy(
onehot_labels=outputs, logits=model)
# 2. valid usage: `softmax_cross_entropy_with_logits_v2`
# with predicted logits values
valid1 = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=outputs, logits=logits))
# 3. (recommended) valid usage: `softmax_cross_entropy`
# with predicted logits values
valid2 = tf.losses.softmax_cross_entropy(
onehot_labels=outputs, logits=logits)
preds, invalid, valid1, valid2 = sess.run(
[model, invalid, valid1, valid2],
{inputs: model.preprocess(img),
outputs: label})
sess.close()
# Check the results with desired outputs
# cross entropy loss: -\sum_i p_i \log{\hat{p_i}}
desired = -np.sum(label * np.log(preds))
np.testing.assert_allclose(valid1, desired)
np.testing.assert_allclose(valid2, desired)
try:
np.testing.assert_allclose(invalid, desired)
except AssertionError as e:
print(e)
Hi,
I would like to ask about the parameter settings of training MobileNet V2 from scratch with ImageNet, such as the initial learning rate, the optimizer, the loss function, as well as the training epochs.
Thank you so much.
Hi, is it possible to load custom weights in tensornets models? Say I have some different classes than the ones in coco dataset for a yolov3 model and I'd like to load them using tensornets. Is that doable?
Thanks in advance!
First off, thanks very much for all your work on this - a really clean and understandable implementation of YOLO 😄 I have a few questions (probably very basic) about getting started with training and inference with tensornets - in specific to the YOLOv2 model:
stopbackward=1
?Thanks very much!
I am trying to work on transfer learning using Mobilenet Pretrained model
model = nets.MobileNet35v2(inputs, is_training=True, classes=2)
with tf.Session() as sess:
nets.pretrained(model)
But this gives the following error
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-ba2d4690d272> in <module>
8
9 with tf.Session() as sess:
---> 10 nets.pretrained(model)
11
12 for i in range(0,len(epochs)):
~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensornets/pretrained.py in assign(scopes)
56 model_name = parse_scopes(scope)[0]
57 try:
---> 58 __load_dict__[model_name](scope)
59 except KeyError:
60 try:
~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensornets/pretrained.py in load_mobilenet35v2(scopes, return_fn)
550 file_hash='cf758f7f8024d39365e553ec924bb395')
551 values = parse_weights(weights_path)
--> 552 return return_fn(scopes, values)
553
554
~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensornets/pretrained.py in _assign(scopes, values)
108
109 for scope in scopes:
--> 110 sess.run(pretrained_initializer(scope, values))
111
112
~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensornets/utils.py in pretrained_initializer(scope, values)
324 'Never mind if you are trying to load stem layers only.')
325
--> 326 if scope.dtype == tf.float16:
327 ops = [weights[0].assign(np.asarray(values[0], dtype=np.float16))]
328 for (w, v) in zip(weights[1:-2], values[1:-2]):
AttributeError: 'str' object has no attribute 'dtype'
hi with windows got this error when trying your detection script
is there something missing.
installed with pip
AssertionError: get_boxes
requires darkflow
When multiple models exist, pretrained()
from the second doesn't work.
import tensorflow as tf
import tensornets as nets
inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
model1 = nets.ResNet50(inputs, scope='abc')
model2 = nets.Inception2(inputs, scope='def')
model3 = nets.DenseNet121(inputs, scope='ghi')
with tf.Session() as sess:
sess.run(model1.pretrained()) # O
try:
sess.run(model2.pretrained()) # X
except:
print('X')
try:
sess.run(model3.pretrained()) # X
except:
print('X')
when I use nets.pretrained(model)
, error happends
if scope.dtype == tf.float16:
AttributeError: 'str' object has no attribute 'dtype'
I think it would be nice to add support of this model https://github.com/openimages/dataset
I want to train using yolov3. how can i use the module yolov3 to train ?
TensorNets provides a seamless integration with regular TensorFlow APIs. You can define any models under tf.variable_scope
and tf.name_scope
to couple the model with your established scripts. This document shows basic examples for tf.variable_scope
, tf.name_scope
, and weight sharing. First, import two libraries:
import tensorflow as tf
import tensornets as nets
Let's get started with basic TensorFlow APIs. You can manage a prefix of variable names with tf.variable_scope
and tf.name_scope
. The difference is that tf.Variable
will be affected by only tf.name_scope
, while tf.Tensor
by both tf.variable_scope
and tf.name_scope
. Also, the second tf.get_variable('w', [1])
will try to create the same variable if tf.variable_scope(reuse=None)
, or return the pointer of the existing variable otherwise (reuse=True
, reuse=tf.AUTO_REUSE
). Here is an example:
with tf.name_scope('foo'):
with tf.variable_scope('goo'):
with tf.name_scope('hoo'):
# `tf.Variable` will be affected by only `tf.name_scope`.
w = tf.get_variable('w', [1])
assert w.name == 'goo/w:0'
# `tf.Tensor` will be affected by both `tf.variable_scope` and `tf.name_scope`.
s = tf.constant(-1.0)
y = s * w
assert s.name == 'foo/goo/hoo/Const:0'
assert y.name == 'foo/goo/hoo/mul:0'
# `tf.get_variable` will try to create the same variable again
# if `tf.variable_scope(reuse=None)` (default).
try:
w2 = tf.get_variable('w', [1])
except ValueError as e:
print(e) # Variable goo/w already exists, disallowed.
The principle is easily extended to TensorNets. The weights returned by get_weights
are tf.Variable
, and the outputs from get_outputs
and get_middles
are tf.Tensor
. Thus, the weights will be affected by only tf.name_scope
, while the outputs and the middles by both tf.variable_scope
and tf.name_scope
. Surely, the model function call can't be performed without reuse=True
or reuse=tf.AUTO_REUSE
because the function will try to create the same variable again.
with tf.name_scope('xoo'):
with tf.variable_scope('yoo'):
with tf.name_scope('zoo'):
# The weights returned by `get_weights` are `tf.Variable`,
# and the outputs from `get_outputs` and `get_middles` are `tf.Tensor`
x1 = tf.placeholder(tf.float32, [None, 224, 224, 3], name='x1')
model1 = nets.ResNet50(x1)
# `tf.Variable` will be affected by only `tf.name_scope`.
assert model1.get_weights()[-1].name == 'yoo/resnet50/logits/biases:0'
# `tf.Tensor` will be affected by both `tf.variable_scope` and `tf.name_scope`.
assert model1.name == 'xoo/yoo/zoo/resnet50/probs:0'
assert model1.get_outputs()[-1].name == 'xoo/yoo/zoo/resnet50/probs:0'
assert model1.get_middles()[-1].name == 'xoo/yoo/zoo/resnet50/conv5/block3/out:0'
# `tf.get_variable` will try to create the same variable again
# if `tf.variable_scope(reuse=None)` (default).
try:
x2 = tf.placeholder(tf.float32, [None, 224, 224, 3], name='x2')
model2 = nets.ResNet50(x2)
except ValueError as e:
print(e) # Variable yoo/resnet50/conv1/conv/weights already exists, disallowed.
And we can easily implement the concept of weight sharing by using tf.variable_scope(reuse=tf.AUTO_REUSE)
. An example is as follows:
with tf.variable_scope('boo', reuse=tf.AUTO_REUSE):
w1 = tf.get_variable('w', [1])
w2 = tf.get_variable('w', [1])
assert w1 == w2
assert w1.name == 'boo/w:0'
s = tf.constant(-1.0)
y1 = s * w1
y2 = s * w2
assert y1 != y2
assert y1.name == 'boo/mul:0'
assert y2.name == 'boo/mul_1:0'
TensorNets can be also easily integrated with tf.variable_scope
:
with tf.variable_scope('koo', reuse=tf.AUTO_REUSE):
x1 = tf.placeholder(tf.float32, [None, 224, 224, 3], name='x1')
x2 = tf.placeholder(tf.float32, [None, 224, 224, 3], name='x2')
model1 = nets.ResNet50(x1)
model2 = nets.ResNet50(x2)
for (a, b) in zip(model1.get_weights(), model2.get_weights()):
assert a == b
assert model1.get_weights()[-1].name == 'koo/resnet50/logits/biases:0'
assert model1 != model2
assert model1.name == 'koo/resnet50/probs:0'
assert model2.name == 'koo/resnet50_1/probs:0'
I'd like to say that there are two patterns to implement weight sharing:
tf.variable_scope
:with tf.variable_scope('koo', reuse=tf.AUTO_REUSE):
model1 = nets.ResNet50(x1)
model2 = nets.ResNet50(x2)
variable_scope
:model1 = nets.ResNet50(x1, reuse=tf.AUTO_REUSE)
model2 = nets.ResNet50(x2, reuse=tf.AUTO_REUSE)
import functools
resnet = functools.partial(nets.ResNet50, reuse=tf.AUTO_REUSE)
model1 = resnet(x1)
model2 = resnet(x2)
And I recommend the following pattern used in deploying multiple clones in tf.slim
:
with tf.name_scope('clone0'):
model1 = nets.ResNet50(x1, reuse=tf.AUTO_REUSE)
with tf.name_scope('clone1'):
model2 = nets.ResNet50(x2, reuse=tf.AUTO_REUSE)
for (a, b) in zip(model1.get_weights(), model2.get_weights()):
assert a == b
assert model1.name == 'clone0/resnet50/probs:0'
assert model2.name == 'clone1/resnet50/probs:0'
Without tf.name_scope
, tf.Tensor
will be automatically named with a postfix-style (resnet50
, resnet50_1
, ...). I think that it may be difficult to manage tensor names in such cases.
Hello!
Thank you for sharing this library, it's great work!
I would like to know whether it's possible to use dropout with the pretrained models, I'm obtaining severe overfitting in my models.
Thanks in advance.
Regards,
Amelia.
in get_v2_boxes,
results = yolov2_box(opts, np.array(outs[0], dtype=np.float32))
TypeError: 'NoneType' object is not callable
It seems that it failed to do this step: from .darkflow_utils.get_boxes import yolov2_box
How can I solve it?
used your code as it is to compare multiple nets and get this one
tried with the version your provided hours ago on windows
TypeError Traceback (most recent call last)
in ()
10
11 with tf.Session() as sess:
---> 12 nets.pretrained(models)
13 for (model, img) in zip(models, imgs):
14 preds = sess.run(model, {inputs: img})
c:\anaconda3\lib\site-packages\tensornets\pretrained.py in assign(scopes)
56 model_name = parse_scopes(scope)[0]
57 try:
---> 58 load_dictmodel_name
59 except KeyError:
60 try:
c:\anaconda3\lib\site-packages\tensornets\pretrained.py in load_mobilenet75(scopes, return_fn)
511 cache_subdir='models',
512 file_hash='d4557a46a44eebfeaf08c82ae33765ed')
--> 513 values = parse_weights(weights_path)
514 return return_fn(scopes, values)
515
c:\anaconda3\lib\site-packages\tensornets\utils.py in parse_weights(weights_path, move_rules)
291 if later_tf_version:
292 for (i, name) in enumerate(data['names']):
--> 293 if '/beta' in data['names'][i-1] and '/gamma' in name:
294 values[i], values[i-1] = values[i-1], values[i]
295
TypeError: a bytes-like object is required, not 'str'
Here's the same image put through YOLOv2COCO
and then YOLOv3COCO
respectively (threshold set at 0.4 and input resolution at 416x416 for both):
YOLOv3 does pick up the smaller people (as one would expect) - but for some reason it seems to predicting a much smaller bounding box than it should. I've done some experimenting and similar behavior is exhibited on quite a few images (sometimes the proper box appears and a smaller, inner one also appears - in this case, though, only the smaller one is appearing).
It's possible that this is just due to a poor prediction on the model's part, but my guess is that it isn't and due to some problem with the NMS function (almost as if it's doing non-minimal-suppression...if that's a thing 😄 ) or other post processing step.
Hi ! I was using tensornets with threading.Threads. It shows error with some tensorflow shapes. Please look after this and provide me with the solution.
Te code is
import threading
import tensorflow as tf
import tensornets as nets
import cv2
import numpy as np
import time
import os
import argparse
class MyThread(threading.Thread):
def run(self):
# os.popen("start cmd /k python detection.py")
print("------------------------------{} started!----------------------------------------".format(self.getName())) # "Thread-x started!"
inputs = tf.placeholder(tf.float32, [None, 416, 416, 3])
model = nets.YOLOv3COCO(inputs, nets.Darknet19)
#model = nets.YOLOv2(inputs, nets.Darknet19)
#frame=cv2.imread("D://pyworks//yolo//truck.jpg",1)
cap = cv2.VideoCapture('rtsp://root:[email protected]:554/axis-media/media.amp')
classes={'0':'person','1':'bicycle','2':'car','3':'bike','5':'bus','7':'truck'}
list_of_classes=[0,1,2,3,5,7]
print("Done")
config = tf.ConfigProto()
config.intra_op_parallelism_threads = 44
config.inter_op_parallelism_threads = 44
# tf.session(config=config)
with tf.Session(config=config) as sess:
print("---------------------------{} session running-----------------------".format(self.getName()))
sess.run(model.pretrained())
# Create a VideoCapture object and read from input file
# cap = cv2.VideoCapture('C://Users//essi//Downloads//vehicle.mp4')
# Check if camera opened successfully
if (cap.isOpened()== False):
print("Error opening video file")
co=0
# Read until video is completed
while(cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret == True:
co=co+1
print(co)
img=cv2.resize(frame,(416,416))
if (co%1==0):
imge=np.array(img).reshape(-1,416,416,3)
start_time=time.time()
preds = sess.run(model.preds, {inputs: model.preprocess(imge)})
# preds= nets.preprocess(model,imge)
print("--- %s seconds ---" % (time.time() - start_time))
boxes = model.get_boxes(preds, imge.shape[1:3])
cv2.namedWindow('video feed',cv2.WINDOW_NORMAL)
cv2.resizeWindow('video feed', 700,500)
#print("--- %s seconds ---" % (time.time() - start_time))
boxes1=np.array(boxes)
for j in list_of_classes:
count =0
if str(j) in classes:
lab=classes[str(j)]
if len(boxes1) !=0:
for i in range(len(boxes1[j])):
box=boxes1[j][i]
if boxes1[j][i][4]>=.40:
count += 1
cv2.rectangle(img,(box[0],box[1]),(box[2],box[3]),(0,255,0),1)
cv2.putText(img, lab, (box[0],box[1]), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 255), lineType=cv2.LINE_AA)
print(lab,": ",count)
# Display the resulting frame
cv2.imshow('video feed', img)
# Press Q on keyboard to exit
if cv2.waitKey(40) & 0xFF == ord('q'):
break
# Break the loop
# else:
# break
# When everything done, release
# the video capture object
cap.release()
# Closes all the frames
cv2.destroyAllWindows()
print("------------------------------{} finished!----------------------------------------".format(self.getName())) # "Thread-x finished!"
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-t', help='help')
args = parser.parse_args()
args=vars(args)
print(args)
for x in range(int(args['t'])): # Four times...
mythread = MyThread(name = "Thread-{}".format(x+1)) # ...Instantiate a thread and pass a unique ID to it
mythread.start()
if name == 'main':main()
AND IT SHOWS THE OUTPUT AS
------------------------------Thread-1 started!----------------------------------------
WARNING: Logging before flag parsing goes to stderr.
------------------------------Thread-2 started!----------------------------------------
W0910 10:55:52.911941 5408 deprecation_wrapper.py:119] From .\threadwc.py:21: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
W0910 10:55:52.911941 5184 deprecation_wrapper.py:119] From .\threadwc.py:21: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
W0910 10:55:53.041654 5408 deprecation_wrapper.py:119] From C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py:238: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.
W0910 10:55:53.041654 5184 deprecation_wrapper.py:119] From C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py:238: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.
W0910 10:55:53.647777 5408 deprecation_wrapper.py:119] From C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py:279: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.
Done
2019-09-10 10:55:58.563571: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
---------------------------Thread-2 session running-----------------------
Done
---------------------------Thread-1 session running-----------------------
C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py:319: UserWarning: The sizes of symbolic and actual weights do not match. Never mind if you are trying to load stem layers only.
warnings.warn('The sizes of symbolic and actual weights do not match. '
Exception in thread Thread-2:
Traceback (most recent call last):
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1864, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shapes must be equal rank, but are 4 and 1 for 'Assign_291' (op: 'Assign') with input shapes: [1,1,512,256], [255].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\threading.py", line 916, in _bootstrap_inner
self.run()
File ".\threadwc.py", line 41, in run
sess.run(model.pretrained())
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\pretrained.py", line 99, in _direct
return fun(scope, return_fn=pretrained_initializer)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\pretrained.py", line 695, in load_ref_yolo_v3_coco
return return_fn(scopes, values)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py", line 333, in pretrained_initializer
ops = [w.assign(v) for (w, v) in zip(weights[:-2], values[:-2])]
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py", line 333, in
ops = [w.assign(v) for (w, v) in zip(weights[:-2], values[:-2])]
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\variables.py", line 1952, in assign
name=name)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\state_ops.py", line 227, in assign
validate_shape=validate_shape)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_state_ops.py", line 69, in assign
use_locking=use_locking, name=name)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 3616, in create_op
op_def=op_def)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 2027, in init
control_input_ops)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1867, in _create_c_op
raise ValueError(str(e))
ValueError: Shapes must be equal rank, but are 4 and 1 for 'Assign_291' (op: 'Assign') with input shapes: [1,1,512,256], [255].
Exception in thread Thread-1:
Traceback (most recent call last):
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1864, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shapes must be equal rank, but are 4 and 1 for 'Assign_291' (op: 'Assign') with input shapes: [1,1,512,256], [255].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\threading.py", line 916, in _bootstrap_inner
self.run()
File ".\threadwc.py", line 41, in run
sess.run(model.pretrained())
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\pretrained.py", line 99, in _direct
return fun(scope, return_fn=pretrained_initializer)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\pretrained.py", line 695, in load_ref_yolo_v3_coco
return return_fn(scopes, values)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py", line 333, in pretrained_initializer
ops = [w.assign(v) for (w, v) in zip(weights[:-2], values[:-2])]
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\utils.py", line 333, in
ops = [w.assign(v) for (w, v) in zip(weights[:-2], values[:-2])]
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\variables.py", line 1952, in assign
name=name)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\state_ops.py", line 227, in assign
validate_shape=validate_shape)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_state_ops.py", line 69, in assign
use_locking=use_locking, name=name)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 3616, in create_op
op_def=op_def)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 2027, in init
control_input_ops)
File "C:\Users\essi\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1867, in _create_c_op
raise ValueError(str(e))
ValueError: Shapes must be equal rank, but are 4 and 1 for 'Assign_291' (op: 'Assign') with input shapes: [1,1,512,256], [255].
Hello, facing difficulties to install tensornets.
my environment is explained bellow.
OS: windows 10, windows 7
anaconda
Python version: 3.5, 3.6
tensorflow version: 1.9, 1.10
PIP version: 18.1, 10.0.1, 19.x
pip install tensornets and pip install git+https://github.com/taehoonlee/tensornets.git
Collecting git+https://github.com/taehoonlee/tensornets.git
Cloning https://github.com/taehoonlee/tensornets.git to c:\users\admin\appdata\local\temp\pip-req-build-pslh3n1z
Building wheels for collected packages: tensornets
Building wheel for tensornets (setup.py) ... error
Complete output from command c:\programdata\anaconda3\python.exe -u -c "import setuptools, tokenize;file='C:\Users\admin\AppData\Local\Temp\pip-req-build-pslh3n1z\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" bdist_wheel -d C:\Users\admin\AppData\Local\Temp\pip-wheel-kz3ibhta --python-tag cp35:
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.5
creating build\lib.win-amd64-3.5\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\wavenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets_init_.py -> build\lib.win-amd64-3.5\tensornets
creating build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\coco.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets_init_.py -> build\lib.win-amd64-3.5\tensornets\datasets
creating build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references_init_.py -> build\lib.win-amd64-3.5\tensornets\references
creating build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils_init_.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running egg_info
creating tensornets.egg-info
writing top-level names to tensornets.egg-info\top_level.txt
writing tensornets.egg-info\PKG-INFO
writing dependency_links to tensornets.egg-info\dependency_links.txt
writing manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\datasets\coco.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
Failed building wheel for tensornets
Running setup.py clean for tensornets
Failed to build tensornets
Installing collected packages: tensornets
Running setup.py install for tensornets ... error
Complete output from command c:\programdata\anaconda3\python.exe -u -c "import setuptools, tokenize;file='C:\Users\admin\AppData\Local\Temp\pip-req-build-pslh3n1z\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record C:\Users\admin\AppData\Local\Temp\pip-record-tgoomb1b\install-record.txt --single-version-externally-managed --compile:
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.5
creating build\lib.win-amd64-3.5\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\wavenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets_init_.py -> build\lib.win-amd64-3.5\tensornets
creating build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\coco.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets_init_.py -> build\lib.win-amd64-3.5\tensornets\datasets
creating build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references_init_.py -> build\lib.win-amd64-3.5\tensornets\references
creating build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils_init_.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running egg_info
writing dependency_links to tensornets.egg-info\dependency_links.txt
writing tensornets.egg-info\PKG-INFO
writing top-level names to tensornets.egg-info\top_level.txt
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\datasets\coco.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
----------------------------------------
Command "c:\programdata\anaconda3\python.exe -u -c "import setuptools, tokenize;file='C:\Users\admin\AppData\Local\Temp\pip-req-build-pslh3n1z\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record C:\Users\admin\AppData\Local\Temp\pip-record-tgoomb1b\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in C:\Users\admin\AppData\Local\Temp\pip-req-build-pslh3n1z\
Kindly help
Does anyone know if these pre-trained models can be converted to TensorRT engine executables? Or JIT'd by XLA for NVIDIA GPUs?
The title says it all :)
Though training is successful in multi-gpu cases, eval step fails with the following error:
InvalidArgumentError (see above for traceback): Number of ways to split should evenly divide the split dimension, but got split_dim 0 (size = 1) and num_split 2
[[Node: split_inputs/split = Split[T=DT_FLOAT, num_split=2, _device="/job:localhost/replica:0/task:0/device:CPU:0"](split_inputs/split/split_dim, IteratorGetNext)]]
My program is estimator based, training using train_and_evaluate method. Also using replicate_model_fn to run across multi GPUs
For some reason, tensornets
fails to install when doing pip install -r requirements.txt
on production.
Step #1 - "builder": ModuleNotFoundError: No module named 'numpy'
Step #1 - "builder": import numpy
Step #1 - "builder": File "/env/src/tensornets/setup.py", line 1, in <module>
Step #1 - "builder": File "<string>", line 1, in <module>
Step #1 - "builder": Traceback (most recent call last):
Step #1 - "builder": Complete output from command python setup.py egg_info:
Step #1 - "builder": Cloning https://github.com/taehoonlee/tensornets.git to /env/src/tensornets
Step #1 - "builder": Obtaining tensornets from git+https://github.com/taehoonlee/tensornets.git#egg=tensornets (from -r requirements.txt (line 12))
Step #1 - "builder": Saved /tmp/tmpkyWvcV/wheel/tensorflow-1.15.0rc2-cp37-cp37m-manylinux2010_x86_64.whl
Step #1 - "builder": Downloading https://files.pythonhosted.org/packages/0a/a9/a5ffe715d3475fed0a79be8e13ef638abbd1c013fdbe8f43892f06336b82/tensorflow-1.15.0rc2-cp37-cp37m-manylinux2010_x86_64.whl (412.3MB)
Step #1 - "builder": Collecting tensorflow==1.15.0rc2 (from -r requirements.txt (line 11))
Step #1 - "builder": Saved /tmp/tmpkyWvcV/wheel/Pillow-6.2.0-cp37-cp37m-manylinux1_x86_64.whl
Step #1 - "builder": Downloading https://files.pythonhosted.org/packages/34/b7/fb24ad352b747340ec1975c84c30cca37bb93a0079e037d1f0930afd65ad/Pillow-6.2.0-cp37-cp37m-manylinux1_x86_64.whl (2.1MB)
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For some reason, tensorflow is not able to find numpy. I have tried installing using the distribution package before installing tensorflow, but the same happened. Any idea?
When I execute the following code:
In [1]: import tensorflow as tf
/usr/software/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
imp
In [2]: import tensornets as nets
In [3]: inputs = tf.placeholder(tf.float32, shape=[1, 224, 224, 3])
In [4]: model = nets.DenseNet121(inputs)
In [5]: with tf.Session() as sess:
...: nets.pretrained(model)
I encountered the following errors:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-ba1220a3288a> in <module>()
1 with tf.Session() as sess:
----> 2 nets.pretrained(model)
3
/usr/software/anaconda3/lib/python3.6/site-packages/tensornets-0.1.0-py3.6.egg/tensornets/pretrained.py in pretrained(scopes)
47 model_name = parse_scopes(scope)[0]
48 try:
---> 49 __load_dict__[model_name](scope)
50 except KeyError:
51 found = False
/usr/software/anaconda3/lib/python3.6/site-packages/tensornets-0.1.0-py3.6.egg/tensornets/pretrained.py in load_densenet121(scopes)
280 cache_subdir='models',
281 file_hash='9817430b1d3634645f6b04b8c663c34f')
--> 282 return load_torch_weights(scopes, weights_path)
283
284
/usr/software/anaconda3/lib/python3.6/site-packages/tensornets-0.1.0-py3.6.egg/tensornets/utils.py in load_torch_weights(scopes, weights_path, move_rules)
203 for (i, name) in enumerate(names):
204 if 'running_mean' in name:
--> 205 names[i-1], names[i-2] = names[i-2], names[i-1]
206
207 values = []
TypeError: 'odict_keys' object does not support indexing
How could I solve this problem? Thanks!
Hi! Thanks a lot for this repo, I think it's very well written and extremely easy to use!
I am training a simple MNIST multi-class classifier using pretrained resnet50. I am using a placeholder 'is_train' to set the 'is_training' argument in the resnet50 model to True (during training) and False (during validation). However, when I set it's value to False during validation, my validation loss is exploding. This is not happening if I set it to True during validation.
Please find the code to replicate this issue below.
import numpy as np
import tensorflow as tf
import tensornets as tfnets
from tensorflow.examples.tutorials.mnist import input_data
np.random.seed(0)
graph = tf.Graph()
with graph.as_default():
mnist = input_data.read_data_sets("mnist_data/", one_hot=False)
x = tf.placeholder(tf.float32, [None, 256, 256, 3])
y = tf.placeholder(tf.int32, [None])
is_train = tf.placeholder(tf.bool)
optimizer = tf.train.AdamOptimizer()
model = tfnets.ResNet50(x, is_training = is_train, classes = 10)
logits = model.get_outputs()[-2]
probs = tf.nn.softmax(logits)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y, logits = logits))
grads = optimizer.compute_gradients(loss)
train_op = optimizer.apply_gradients(grads)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(model.pretrained())
print('Training ...')
batch_size = 32
for iteration in range(20):
_x, _y = mnist.train.next_batch(batch_size)
_x = np.resize(np.stack((np.reshape(_x, (-1, 28, 28)),)*3, axis = -1), (batch_size, 256, 256, 3))
_, lossval = sess.run([train_op, loss], feed_dict = {x:_x, y:_y, is_train:True})
print(iteration, lossval)
print('Validation ...')
for iteration in range(5):
_x, _y = mnist.validation.next_batch(batch_size)
_x = np.resize(np.stack((np.reshape(_x, (-1, 28, 28)),)*3, axis = -1), (batch_size, 256, 256, 3))
lossval = sess.run(loss, feed_dict = {x:_x, y:_y, is_train:False})
print(iteration, lossval)
The output I am getting when I set 'is_train' to False while calculating validation loss is as follows:
Training ...
0 2.5664392
1 2.5185232
2 2.6779196
3 2.904881
4 2.5281467
5 2.3716216
6 2.6480408
7 2.4264488
8 2.5613265
9 2.6664178
10 2.407669
11 2.588695
12 2.4091241
13 2.6468048
14 2.6137376
15 2.3654165
16 2.3985069
17 2.302301
18 2.3154862
19 2.643575
Validation ...
0 3006.0938
1 3228.6968
2 3172.6226
3 2331.4446
4 2426.8574
However, when I set 'is_train' to True while calculating validation loss, the output is as follows:
Training ...
0 2.6028776
1 2.4225206
2 2.7544856
3 2.638253
4 2.4008565
5 2.642138
6 2.3750484
7 2.840887
8 2.3587594
9 2.6521862
10 2.450266
11 2.3560605
12 2.3225245
13 2.4954278
14 2.3524
15 2.3139462
16 2.2738576
17 2.4993274
18 2.3943353
19 2.7758856
Validation ...
0 2.2924833
1 2.396636
2 2.4049609
3 2.3891609
4 2.3445334
It would be great if you could let me know what's going wrong here. I am using tensorflow 1.13 and python 3.6. Thanks!
Another issue, this time with python2
Python 2.7.13 |Anaconda 4.3.1 (64-bit)| (default, Dec 20 2016, 23:09:15)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> import tensorflow as tf
>>> import tensornets as nets
>>> import numpy as np
>>>
>>> print(tf.__version__)
1.2.1
>>> print(np.__version__)
1.13.1
>>>
>>> inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
>>> outputs = tf.placeholder(tf.float32, [None, 50])
>>> model = nets.ResNet152v2(inputs, is_training=True, classes=50)
>>>
>>> loss = tf.losses.softmax_cross_entropy(outputs, model)
>>> train = tf.train.AdamOptimizer(learning_rate=1e-5).minimize(loss)
>>>
>>> with tf.Session() as sess:
... nets.pretrained(model)
...
2017-11-15 21:42:48.330744: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-15 21:42:48.330814: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-15 21:42:48.330941: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-11-15 21:42:48.595931: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-11-15 21:42:48.596442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:01:00.0
Total memory: 10.91GiB
Free memory: 9.73GiB
2017-11-15 21:42:48.741859: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x121ad770 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-11-15 21:42:48.742280: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-11-15 21:42:48.742780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 1 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:02:00.0
Total memory: 10.91GiB
Free memory: 10.75GiB
2017-11-15 21:42:48.743591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 1
2017-11-15 21:42:48.743609: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y Y
2017-11-15 21:42:48.743619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: Y Y
2017-11-15 21:42:48.743699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0)
2017-11-15 21:42:48.743713: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/shared/miha/py_homebrew/tensornets/pretrained.py", line 49, in pretrained
__load_dict__[model_name](scope)
File "/shared/miha/py_homebrew/tensornets/pretrained.py", line 171, in load_resnet152v2
return load_weights(scopes, weights_path)
File "/shared/miha/py_homebrew/tensornets/utils.py", line 175, in load_weights
assert len(weights) == len(values), 'The sizes of symbolic and ' \
AssertionError: The sizes of symbolic and actual weights do not match.
>>>
import tensorflow as tf
import tensornets as nets
inputs = tf.placeholder(tf.float32, [None, 416, 416, 3])
model = nets.YOLOv2(inputs, nets.Darknet19)
img = nets.utils.load_img('cat.png')
with tf.Session() as sess:
sess.run(model.pretrained())
preds = sess.run(model, {inputs: model.preprocess(img)})
boxes = model.get_boxes(preds, img.shape[1:3])
from tensornets.datasets import voc
print("%s: %s" % (voc.classnames[7], boxes[7][0])) # 7 is cat
Traceback (most recent call last):
File "main.py", line 14, in <module>
from tensornets.datasets import voc
File "/usr/local/lib/python3.5/dist-packages/tensornets/datasets/__init__.py", line 1, in <module>
import voc
ImportError: No module named 'voc'
Commenting out the first (and only) line in /usr/local/lib/python3.5/dist-packages/tensornets/datasets/__init__.py
fixes the issue. A real fix would probably deal with absolute and relative imports though.
python3.5
tensorflow (1.6.0)
tensorflow-gpu (1.6.0)
tensorflow-tensorboard (0.4.0rc3)
tensornets (0.3.1)
darkflow (1.0.0)
Hello,
I've noticed a minor issue when using name scopes (mostly for Tensorboard visualization). For instance the following code raises an error in the utils.var_scope
wrapper:
with tf.name_scope('A'):
with tf.variable_scope('B'):
x = tf.placeholder(shape=(None, 1024, 1024, 3), dtype=tf.float32)
y = tensornets.MobileNet100(x, stem=True)
It seems the problem comes from line 243 where _scope = tf.get_variable_scope().name
only looks at variable scope while the tf.get_collection(..., scope=_scope)
at line 124 look through variable names.
It can be solved by replacing line 243 with _scope = tf.contrib.framework.get_name_scope()
, but I'm not sure if it's the best workaround.
For getting more accuracy in faster-rcnn , which parameters i have to tune (tuning parameters). please anybody provide the solution for that problem, actually my model is working but the problem is getting less accuracy and some miss classifications are happens , so please kindly provide solution for getting high accuracy in the model.
Thanking you!
When using YOLOv2 COCO or VOC on tensornets (TF 1.7, CPU, Win 10) the memory usage with the input as tf.float32
is massive (3GB +) which is more than twice what darkflow uses for the same model. Not quite sure why.
When trying to use tf.float16
with YOLOv2, memory usage is significantly reduced (could be due to a bug that so little memory is used actually...), but there seems to be a problem with the Cython utils ported from darkflow handling the FP16 output data.
I get the following error:
Traceback (most recent call last):
File "blah\src\test.py", line 15, in
boxes = model.get_boxes(preds, img.shape[1:3])
File "C:\Users\abags\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\references\yolos.py", line 189, in _get_boxes
return get_v2_boxes(opts('yolov2'), *args, **kwargs)
File "C:\Users\abags\AppData\Local\Programs\Python\Python36\lib\site-packages\tensornets\references\yolo_utils.py", line 94, in get_v2_boxes
results = yolov2_box(opts, outs[0].copy())
File "tensornets\references\darkflow_utils\get_boxes.pyx", line 108, in tensornets.references.darkflow_utils.get_boxes.yolov2_box
ValueError: Does not understand character buffer dtype format string ('e')
"from tensornets import utils
img = utils.load_img('cat.png', target_size=256, crop_size=224)
assert img.shape == (1, 224, 224, 3)"
This script get error.
AttributeError Traceback (most recent call last)
in ()
1 from tensornets import utils
----> 2 img = utils.load_img('cat.png', target_size=256, crop_size=224)
3 assert img.shape == (1, 224, 224, 3)
~/.conda/envs/tensor/lib/python3.6/site-packages/tensornets/utils.py in load_img(path, grayscale, target_size, crop_size, interp)
176 if isinstance(target_size, int):
177 hw_tuple = tuple([x * target_size // min(img.shape[:2])
--> 178 for x in img.shape[1::-1]])
179 else:
180 hw_tuple = (target_size[1], target_size[0])
AttributeError: 'NoneType' object has no attribute 'shape'
Sorry for all the issues opened recently 😄 No worries if you don't have time to look at this one.
Running with: Ubuntu 17.10, Python 2.7, Tensorflow-GPU 1.7.0, Nvidia K80
The following code:
import tensorflow as tf
import tensornets as nets
inputs = tf.placeholder(tf.float32, [None, 416, 416, 3])
model = nets.YOLOv3VOC(inputs)
img = nets.utils.load_img('cat.png')
with tf.Session() as sess:
sess.run(model.pretrained())
preds = sess.run(model, {inputs: model.preprocess(img)})
boxes = model.get_boxes(preds, img.shape[1:3])
Produces this error:
ValueErrorTraceback (most recent call last)
in ()
10 sess.run(model.pretrained())
11 preds = sess.run(model, {inputs: model.preprocess(img)})
---> 12 boxes = model.get_boxes(preds, img.shape[1:3])/usr/local/lib/python2.7/dist-packages/tensornets/references/yolos.pyc in _get_boxes(*args, **kwargs)
177 def yolov3voc(x, is_training=False, scope=None, reuse=None):
178 def _get_boxes(*args, **kwargs):
--> 179 return get_v3_boxes(opts('yolov3voc'), *args, **kwargs)
180 x = yolov3(x, [1, 2, 8, 8, 4, 3], 75, is_training, scope, reuse)
181 x.get_boxes = _get_boxes/usr/local/lib/python2.7/dist-packages/tensornets/references/yolo_utils.pyc in get_v3_boxes(opts, outs, source_size, threshold)
77 opts['anchor_idx'] = 6 - 3 * i
78 o = np.array(outs[i][0], dtype=np.float32)
---> 79 results = yolov3_box(opts, o)
80 for b in results:
81 idx, box = parse_box(b, threshold, w, h)/usr/local/lib/python2.7/dist-packages/tensornets/references/darkflow_utils/get_boxes.pyx in tensornets.references.darkflow_utils.get_boxes.yolov3_box()
53 @cython.boundscheck(False) # turn off bounds-checking for entire function
54 @cython.wraparound(False) # turn off negative index wrapping for entire function
---> 55 def yolov3_box(meta,np.ndarray[float,ndim=3] net_out_in):
56 cdef:
57 np.intp_t H, W, _, C, B, row, col, box_loop, class_loopValueError: Buffer has wrong number of dimensions (expected 3, got 2)
Any ideas? Same thing happens if I run with YOLOv3COCO
Since tensorflow.contrib methods were moved to tensorflow.core, I'm not able to run tensornets code for YOLOv3COCO.
TensorNets lacks of FP16 supports. For example, the following should work:
inputs = tf.placeholder(tf.float16, [None, 224, 224, 3])
model = nets.ResNet50(inputs)
to process multiple image in one shot i did like this
listim=[
'c:/graph/1.jpg','c:/graph/2.jpg','c:/graph/3.jpg'
]
img = utils.load_img(listim, target_size=256, crop_size=224)
imgs = nets.preprocess(models, img)
got TypeError: bad argument type for built-in operation on the utils.load_img(
how to proceed if multiple image to detect?
i was trying the network comparison and so i thought it could be possible to load multiple image as it is possible to load multiple networks in the for (model, img) in zip(models, imgs):
The 2x down-sampling is one of the important operations in reference models. But, a convolution or a pooling with stride=2, padding='SAME'
may result in different outputs over different deep learning libraries (e.g., TensorFlow, CNTK, Theano, Caffe, Torch, ...) due to their different padding behaviors.
For example (TensorNets syntax; but can be regarded as pseudo codes for other libraries),
x = tf.placeholder(tf.float32, shape=[None, 224, 224, 3])
x = conv(x, 64, 7, stride=2, padding='SAME')
produces a [None, 112, 112, 64]
map. This example can be performed as either one of the following cases:
kernel_size // 2 - 1
, bottom-right: kernel_size // 2
x = pad(x, [[0, 0], [2, 3], [2, 3], [0, 0]]) # 224 -> 229
x = conv(x, 64, 7, stride=2, padding='VALID') # 229 -> 112
kernel_size // 2
x = pad(x, [[0, 0], [3, 3], [3, 3], [0, 0]]) # 224 -> 230
x = conv(x, 64, 7, stride=2, padding='VALID') # 230 -> 112 with slicing the the rightmost 1 pixel
As TensorNets translates the original repositories written in various libraries, TensorNets must consider the two padding behaviors to reproduce the original results exactly.
I compared the performance differences of the two padding schemes with the 11 ResNet variants. Precisely, the two for the ResNets are:
# Case 1 (asymmetric)
x = conv2d(x, 64, 7, stride=2, padding='SAME') # symmetric for ResNet50,101,152v2
...
x = max_pool2d(x, 3, stride=2, padding='SAME')
...
# Case 2 (symmetric)
x = pad(x, [[0, 0], [3, 3], [3, 3], [0, 0]])
x = conv2d(x, 64, 7, stride=2, padding='VALID')
...
x = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]])
x = max_pool2d(x, 3, stride=2, padding='VALID')
...
The results are summarized as follows:
Top-1 | Top-5 | 10-5 | Top-1 | Top-5 | 10-5 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Case 1 (asymmetric) | Case 2 (symmetric) | |||||||||||
ResNet50 | 25.436 | 8.098 | 6.950 | 25.126 | 7.982 | 6.842 | ||||||
ResNet101 | 24.250 | 7.402 | 6.210 | 23.580 | 7.214 | 6.092 | ||||||
ResNet152 | 23.860 | 7.098 | 6.068 | 23.396 | 6.882 | 5.908 | ||||||
ResNet50v2 | 24.040 | 6.966 | 5.896 | 24.526 | 7.252 | 6.012 | ||||||
ResNet101v2 | 22.766 | 6.184 | 5.158 | 23.116 | 6.488 | 5.230 | ||||||
ResNet152v2 | 21.968 | 5.838 | 4.900 | 22.236 | 6.080 | 4.960 | ||||||
ResNet200v2 | 22.286 | 6.056 | 4.902 | 21.714 | 5.848 | 4.830 | ||||||
ResNeXt50c32 | 22.806 | 6.460 | 5.492 | 22.260 | 6.190 | 5.410 | ||||||
ResNeXt101c32 | 21.660 | 6.068 | 4.996 | 21.270 | 5.706 | 4.842 | ||||||
ResNeXt101c64 | 20.924 | 5.566 | 4.666 | 20.506 | 5.408 | 4.564 | ||||||
WideResNet50 | 22.516 | 6.344 | 5.248 | 21.982 | 6.066 | 5.116 |
All except ResNet50,101,152v2 showed better performances in symmetric padding than asymmetric padding. This is because only TensorFlow (as far as I know) uses asymmetric padding, and only ResNet50,101,152v2 are trained with TensorFlow. Note that
Caffe uses definitely symmetric padding, and I can infer that (Py)Torch also does so (I'm not familiar with Torch). Thus, in order to reproduce the original results, I revised the current symmetric paddings for the pool1
in the ResNets as asymmetric paddings only in case of ResNet50,101,152v2. As the conv1
of ResNet50,101,152v2 transforms 299 to 150, the SAME
padding is equivalent to the symmetric so the conv1/pad
didn't be touched. Please see the commit :)
Hello,
I was trying to test your code and got stuck on loading models. See full log:
Python 3.5.2 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:53:06)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> import tensornets as nets
>>> import numpy as np
>>>
>>> print(tf.__version__)
1.3.0
>>> print(np.__version__)
1.13.3
>>>
>>> inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
>>> outputs = tf.placeholder(tf.float32, [None, 50])
>>> model = nets.Inception4(inputs, is_training=True, classes=50)
>>>
>>> loss = tf.losses.softmax_cross_entropy(outputs, model)
>>> train = tf.train.AdamOptimizer(learning_rate=1e-5).minimize(loss)
>>>
>>> with tf.Session() as sess:
... nets.pretrained(model)
...
2017-11-15 21:14:22.616901: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-15 21:14:22.616976: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-15 21:14:22.617005: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-11-15 21:14:22.767544: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-11-15 21:14:22.767871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:01:00.0
Total memory: 10.91GiB
Free memory: 355.38MiB
2017-11-15 21:14:22.900139: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xbea1700 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-11-15 21:14:22.900342: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-11-15 21:14:22.900694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 1 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:02:00.0
Total memory: 10.91GiB
Free memory: 400.44MiB
2017-11-15 21:14:22.900850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 1
2017-11-15 21:14:22.900869: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y Y
2017-11-15 21:14:22.900881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 1: Y Y
2017-11-15 21:14:22.900899: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0)
2017-11-15 21:14:22.900914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0)
Traceback (most recent call last):
File "/shared/miha/programs/miniconda3/lib/python3.5/site-packages/numpy/lib/format.py", line 640, in read_array
array = pickle.load(fp, **pickle_kwargs)
UnicodeDecodeError: 'ascii' codec can't decode byte 0xb1 in position 0: ordinal not in range(128)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/shared/miha/py_homebrew/tensornets/pretrained.py", line 49, in pretrained
__load_dict__[model_name](scope)
File "/shared/miha/py_homebrew/tensornets/pretrained.py", line 101, in load_inception4
return load_weights(scopes, weights_path)
File "/shared/miha/py_homebrew/tensornets/utils.py", line 166, in load_weights
values = data['values']
File "/shared/miha/programs/miniconda3/lib/python3.5/site-packages/numpy/lib/npyio.py", line 233, in __getitem__
pickle_kwargs=self.pickle_kwargs)
File "/shared/miha/programs/miniconda3/lib/python3.5/site-packages/numpy/lib/format.py", line 646, in read_array
"to numpy.load" % (err,))
UnicodeError: Unpickling a python object failed: UnicodeDecodeError('ascii', b'\xb1\xe0\xd7\xad\xdbf\x93\xae\xdcS\x90\xbe\x1d\x8f!?\x81\xdd^\xbf\x9a`\x9f=di\xae\xae 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0, 1, 'ordinal not in range(128)')
You may need to pass the encoding= option to numpy.load
I'm using the latest version of tensornets. Any clue why this is happening?
This is the output when running pip install tensornets
Collecting tensornets
Downloading https://files.pythonhosted.org/packages/b9/b7/dd956d687d5a45ccac1275dba1f522f6dedeaab43e5e540c2627fa4d6f9c/tensornets-0.3.4.tar.gz (576kB)
Building wheels for collected packages: tensornets
Running setup.py bdist_wheel for tensornets: started
Running setup.py bdist_wheel for tensornets: finished with status 'error'
Complete output from command c:\users\jmorales\appdata\local\programs\python\python35\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\jmorales\\AppData\\Local\\Temp\\pip-install-emz1qnku\\tensornets\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d C:\Users\jmorales\AppData\Local\Temp\pip-wheel-7vgpy5t9 --python-tag cp35:
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.5
creating build\lib.win-amd64-3.5\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\__init__.py -> build\lib.win-amd64-3.5\tensornets
creating build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\__init__.py -> build\lib.win-amd64-3.5\tensornets\datasets
creating build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\__init__.py -> build\lib.win-amd64-3.5\tensornets\references
creating build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\__init__.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running egg_info
writing tensornets.egg-info\PKG-INFO
writing top-level names to tensornets.egg-info\top_level.txt
writing dependency_links to tensornets.egg-info\dependency_links.txt
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\darkflow_utils\__init__.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
creating build\temp.win-amd64-3.5
creating build\temp.win-amd64-3.5\Release
creating build\temp.win-amd64-3.5\Release\tensornets
creating build\temp.win-amd64-3.5\Release\tensornets\references
creating build\temp.win-amd64-3.5\Release\tensornets\references\darkflow_utils
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ic:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages\numpy\core\include -Ic:\users\jmorales\appdata\local\programs\python\python35\include -Ic:\users\jmorales\appdata\local\programs\python\python35\include "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10240.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tctensornets/references/darkflow_utils/nms.c /Fobuild\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj
nms.c
c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
tensornets/references/darkflow_utils/nms.c(2380): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2389): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2439): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22288): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22294): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:c:\users\jmorales\appdata\local\programs\python\python35\libs /LIBPATH:c:\users\jmorales\appdata\local\programs\python\python35\PCbuild\amd64 "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10240.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" m.lib /EXPORT:PyInit_nms build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj /OUT:build\lib.win-amd64-3.5\tensornets\references\darkflow_utils\nms.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils\nms.cp35-win_amd64.lib
LINK : fatal error LNK1181: cannot open input file 'm.lib'
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\link.exe' failed with exit status 1181
----------------------------------------
Failed building wheel for tensornets
Running setup.py clean for tensornets
Failed to build tensornets
Installing collected packages: tensornets
Running setup.py install for tensornets: started
Running setup.py install for tensornets: finished with status 'error'
Complete output from command c:\users\jmorales\appdata\local\programs\python\python35\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\jmorales\\AppData\\Local\\Temp\\pip-install-emz1qnku\\tensornets\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record C:\Users\jmorales\AppData\Local\Temp\pip-record-gqdxhlji\install-record.txt --single-version-externally-managed --compile:
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.5
creating build\lib.win-amd64-3.5\tensornets
copying tensornets\capsulenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\darknets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\densenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\detections.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\inceptions.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\layers.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\middles.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\mobilenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\nasnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\ops.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\preprocess.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\pretrained.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\resnets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\squeezenets.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\utils.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\vggs.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\zf.py -> build\lib.win-amd64-3.5\tensornets
copying tensornets\__init__.py -> build\lib.win-amd64-3.5\tensornets
creating build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\imagenet.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\voc.py -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\datasets\__init__.py -> build\lib.win-amd64-3.5\tensornets\datasets
creating build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rcnns.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\rpn_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolos.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\yolo_utils.py -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\__init__.py -> build\lib.win-amd64-3.5\tensornets\references
creating build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\__init__.py -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running egg_info
writing tensornets.egg-info\PKG-INFO
writing top-level names to tensornets.egg-info\top_level.txt
writing dependency_links to tensornets.egg-info\dependency_links.txt
reading manifest file 'tensornets.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensornets.egg-info\SOURCES.txt'
copying tensornets\references\darkflow_utils\get_boxes.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.c -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\datasets\voc.names -> build\lib.win-amd64-3.5\tensornets\datasets
copying tensornets\references\coco.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\voc.names -> build\lib.win-amd64-3.5\tensornets\references
copying tensornets\references\darkflow_utils\__init__.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\box.pyc -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\get_boxes.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pxd -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.pyx -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
copying tensornets\references\darkflow_utils\nms.so -> build\lib.win-amd64-3.5\tensornets\references\darkflow_utils
running build_ext
building 'tensornets.references.darkflow_utils.nms' extension
creating build\temp.win-amd64-3.5
creating build\temp.win-amd64-3.5\Release
creating build\temp.win-amd64-3.5\Release\tensornets
creating build\temp.win-amd64-3.5\Release\tensornets\references
creating build\temp.win-amd64-3.5\Release\tensornets\references\darkflow_utils
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Ic:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages\numpy\core\include -Ic:\users\jmorales\appdata\local\programs\python\python35\include -Ic:\users\jmorales\appdata\local\programs\python\python35\include "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10240.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tctensornets/references/darkflow_utils/nms.c /Fobuild\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj
nms.c
c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
tensornets/references/darkflow_utils/nms.c(2380): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2389): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(2439): warning C4244: '=': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22288): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
tensornets/references/darkflow_utils/nms.c(22294): warning C4244: 'initializing': conversion from 'double' to 'float', possible loss of data
C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:c:\users\jmorales\appdata\local\programs\python\python35\libs /LIBPATH:c:\users\jmorales\appdata\local\programs\python\python35\PCbuild\amd64 "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\LIB\amd64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.10240.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\8.1\lib\winv6.3\um\x64" m.lib /EXPORT:PyInit_nms build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils/nms.obj /OUT:build\lib.win-amd64-3.5\tensornets\references\darkflow_utils\nms.cp35-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.5\Release\tensornets/references/darkflow_utils\nms.cp35-win_amd64.lib
LINK : fatal error LNK1181: cannot open input file 'm.lib'
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\link.exe' failed with exit status 1181
----------------------------------------
Command "c:\users\jmorales\appdata\local\programs\python\python35\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\jmorales\\AppData\\Local\\Temp\\pip-install-emz1qnku\\tensornets\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record C:\Users\jmorales\AppData\Local\Temp\pip-record-gqdxhlji\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in C:\Users\jmorales\AppData\Local\Temp\pip-install-emz1qnku\tensornets\
This is my setup:
Python version: 3.5
OS: Windows 10 Home
System Type: 64-bit Operating System, x64-based processor
Processor: AMD A10-8700P Radeon R6
@taehoonlee I tried to use sess.run(net.pretrained()) for vgg16, but it failed because of shape mismatch (I did not use the default input size). I read the source code and I am confused by line 320 in tensornets/utils.py,
is the code at line 321 designed for tackling the shape mismatch of fcs? If yes, I think it has a problem on vgg16 because it has three fc and this code seems to be only suitable for networks with 1 fc.
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