giphy / celeb-detection-oss Goto Github PK

I followed your instruction on training and added 2 images in example_experiment (1 of brad pitt and 1 of lenna soderberg). I also hard coded the full directory paths within example_experiment.py in case there was a problem there.

Here is my error:

python3 experiments/example_experiment.py 
2019-03-09 11:32:15,174:INFO: GOING TO PROCESS 4 images out of 4
2019-03-09 11:32:15.216302: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
expected str, bytes or os.PathLike object, not NoneType
multiprocessing.pool.RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 44, in mapstar
    return list(map(*args))
  File "/media/r/edata1/code/nns/face_det/celeb-detection-oss/ve-cdoss/lib/python3.6/site-packages/celebrity_detection_model_train-1.0.0-py3.6.egg/model_training/preprocessors/datasets_builder.py", line 119, in process_images_batch
    gpu_memory_fraction=(1 / builder.pool_size)
  File "/media/r/edata1/code/nns/face_det/celeb-detection-oss/ve-cdoss/lib/python3.6/site-packages/celebrity_detection_model_train-1.0.0-py3.6.egg/model_training/preprocessors/face_detection/face_detector.py", line 49, in __init__
    data_dir, 'face_detection'
  File "/media/r/edata1/code/nns/face_det/celeb-detection-oss/ve-cdoss/lib/python3.6/posixpath.py", line 99, in join
    genericpath._check_arg_types('join', a, *p)
  File "/media/r/edata1/code/nns/face_det/celeb-detection-oss/ve-cdoss/lib/python3.6/genericpath.py", line 149, in _check_arg_types
    (funcname, s.__class__.__name__)) from None
TypeError: join() argument must be str or bytes, not 'NoneType'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "experiments/example_experiment.py", line 57, in <module>
    datasets_builder.perform()
  File "/media/r/edata1/code/nns/face_det/celeb-detection-oss/ve-cdoss/lib/python3.6/site-packages/celebrity_detection_model_train-1.0.0-py3.6.egg/model_training/preprocessors/datasets_builder.py", line 62, in perform
    self._process_images(train_images, self.train_dataset_path)
  File "/media/r/edata1/code/nns/face_det/celeb-detection-oss/ve-cdoss/lib/python3.6/site-packages/celebrity_detection_model_train-1.0.0-py3.6.egg/model_training/preprocessors/datasets_builder.py", line 85, in _process_images
    p.map(self.process_images_batch, process_params)
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 288, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 670, in get
    raise self._value
TypeError: join() argument must be str or bytes, not 'NoneType'

Any ideas?

i'm trying to train using GPU but whole process seems to stuck on this line
2019-05-16 14:20:08.050652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
been waiting for hours nothing is happening gpu utilization 0% i included full log down here

2019-05-16 14:20:07,148:INFO: GOING TO PROCESS 752 images out of 752
2019-05-16 14:20:07.169757: 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.
2019-05-16 14:20:07.169763: 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.
2019-05-16 14:20:07.169825: 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.
2019-05-16 14:20:07.169760: 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.
2019-05-16 14:20:07.169833: 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.
2019-05-16 14:20:07.169832: 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.
2019-05-16 14:20:07.169839: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169841: 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.
2019-05-16 14:20:07.169820: 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.
2019-05-16 14:20:07.169844: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169847: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169765: 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.
2019-05-16 14:20:07.169852: 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.
2019-05-16 14:20:07.169856: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169861: 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.
2019-05-16 14:20:07.169862: 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.
2019-05-16 14:20:07.169867: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169870: 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.
2019-05-16 14:20:07.169873: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169876: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169887: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169916: 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.
2019-05-16 14:20:07.169943: 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.
2019-05-16 14:20:07.169949: 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.
2019-05-16 14:20:07.169934: 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.
2019-05-16 14:20:07.169954: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169962: 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.
2019-05-16 14:20:07.169965: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169968: 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.
2019-05-16 14:20:07.169976: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169983: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169767: 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.
2019-05-16 14:20:07.170119: 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.
2019-05-16 14:20:07.170133: 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.
2019-05-16 14:20:07.170144: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.170155: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.169840: 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.
2019-05-16 14:20:07.170370: 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.
2019-05-16 14:20:07.170474: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.170485: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.615536: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.616792: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.84GiB
2019-05-16 14:20:07.616812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.616818: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.616828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.616936: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.621427: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.84GiB
2019-05-16 14:20:07.621474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.621481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.621508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.621694: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.621756: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.621785: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.621878: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.621957: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.622051: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:07.623453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.33GiB
2019-05-16 14:20:07.623482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.623490: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.623506: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.623558: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.33GiB
2019-05-16 14:20:07.623582: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.33GiB
2019-05-16 14:20:07.623598: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.623604: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.623614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.33GiB
2019-05-16 14:20:07.623620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.623631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.623634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.33GiB
2019-05-16 14:20:07.623638: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.623647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.623652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.623652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.623662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.623679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.623684: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.623685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 6.33GiB
2019-05-16 14:20:07.623694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.623706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:07.623713: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:07.623725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
2019-05-16 14:20:07.973430: 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.
2019-05-16 14:20:07.973476: 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.
2019-05-16 14:20:07.973482: 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.
2019-05-16 14:20:07.973487: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:07.973492: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-05-16 14:20:08.050368: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-16 14:20:08.050622: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8095
pciBusID 0000:01:00.0
Total memory: 7.93GiB
Free memory: 7.11GiB
2019-05-16 14:20:08.050637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2019-05-16 14:20:08.050641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2019-05-16 14:20:08.050652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)

trying to python experiments/example_experiment.py

Traceback (most recent call last):
File "experiments/example_experiment.py", line 107, in
trainer.perform()
File "/mnt/data_3/bilguun-ml/celeb-detection-oss/model_training/trainers/trainer.py", line 68, in perform
top_1, top_5 = self._accuracy(prediction, labels, topk=(1, 5))
File "/mnt/data_3/bilguun-ml/celeb-detection-oss/model_training/trainers/trainer.py", line 128, in _accuracy
_, pred = output.topk(maxk, 1, True, True)
RuntimeError: invalid argument 5: k not in range for dimension at /pytorch/aten/src/THC/generic/THCTensorTopK.cu:21

can't find anything on google

Hi
i'd like to do Training & Transfer Learning on my own data using my GPU but it's only using my CPU my GPU utilization 0%
Here is what i have done

• USE_GPU=true in .env
• changed to requirements_gpu.txt in setup.py
  Does the tensorflow-gpu version has to be specific one ? can i use latest ?
  Any suggestions ?

Before running these examples you need to ensure that you downloaded the latest archive with Giphy pretrained models (resources)

download_model.py script tries to download this link but it returns 404:

https://s3.amazonaws.com/giphy-public/models/celeb-detection/resources.tar.gz

This project have lot of conflicts in packages versioning i didn't manage to run python inference.py --image_path media/image.jpg after two 2 days of importing modules errors .
Example of errors :
from google.protobuf.pyext import _message ImportError: DLL load failed
After fixing the error above by following this : Installing protobuf i get that :
numpy.core.umath failed to import
I think that comes from the pip error message : tensorflow 1.15.2 requires numpy<2.0,>=1.16.0, but you'll have numpy 1.15.1 which is incompatible.
Fixing that by upgrading to numpy==1.16.0 lead to that problem :
from numpy.lib.arraypad import _validate_lengths ImportError: cannot import name '_validate_lengths'
fixing that problem by upgrading scikit-image library to 0.15.0 lead to that problem witch i cant fix :

Thank you for helping me because I really need that project !