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A fast tool to do image augmentation on GPU(especially elastic_deform), can be helpful to research on Medical Image.

License: Other

CMake 2.06% Cuda 66.87% Python 31.07%

computer-vision cuda elastic-deform image-augmentation medical-imaging

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minimal working example for elastic_deform

This package is exactly what I need to speed up the data augmentation in my code! Unfortunately, I am unable to get the elastic deformation to work. Here is a tentative minimal working example for the FLAIR image included with the code..

import nibabel as nib
import time
import numpy as np
from cuda_backend.py_api import Handle

img = nib.load('data/FLAIR.nii.gz')
data = img.get_fdata().astype('float32')

start = time.time()
cuda_handle = Handle(data.shape, RGB=False, mode='constant')
cuda_handle.elastic(sigma=1., alpha=20., mode='constant')
cuda_handle.end_flag()
data_aug = cuda_handle.augment(data)
print('%f' % (time.time() - start))

nib.save(nib.Nifti1Image(np.array(data_aug[0]), img.affine, img.header),
         'Flair.deformed.nii.gz')

And here are the input/output:

Any idea what's is going on?

verification question

Could you please update the example so that people can see the verification is successful for augment ? Thank you.

 cuda_handle = Handle(array_image.shape, mode="mirror")
    # cuda_handle.test()
    # cuda_handle.scale(0.5)
    # cuda_handle.flip(do_y=True, do_x=True, do_z=True)
    # cuda_handle.translate(100, 100, 20)
    # cuda_handle.rotate(0.75 * np.pi, 0.75 * np.pi, 0.75 * np.pi)
    cuda_handle.elastic(sigma=5., alpha=200., mode='constant')
    cuda_handle.end_flag()

    # correct_ret = deform.spatial_augment(array_image, mode="mirror")
    # Warm up and Unit test
    for i in range(100):
        output = cuda_handle.augment(array_image, order=1)
    volOut=sitk.GetImageFromArray(output[0])
    sitk.WriteImage( volOut,"data/FLAIR_Elastic.nii.gz", True)
    import ipdb; ipdb.set_trace()
    # check(correct_ret, output[0])

Can I set which GPU to run on manually

Can I set which GPU to run on manually, that is so realistic

Error in cuda 9-2

I am building the code in cuda 9.2 but I got the error

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9220): error: argument of type "const void *" is incompatible with parameter of type "const float *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9231): error: argument of type "const void *" is incompatible with parameter of type "const float *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9244): error: argument of type "const void *" is incompatible with parameter of type "const double *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9255): error: argument of type "const void *" is incompatible with parameter of type "const double *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9268): error: argument of type "const void *" is incompatible with parameter of type "const float *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9279): error: argument of type "const void *" is incompatible with parameter of type "const float *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9292): error: argument of type "const void *" is incompatible with parameter of type "const double *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9220): error: argument of type "const void *" is incompatible with parameter of type "const float *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9303): error: argument of type "const void *" is incompatible with parameter of type "const double *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9231): error: argument of type "const void *" is incompatible with parameter of type "const float *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9316): error: argument of type "const void *" is incompatible with parameter of type "const int *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9244): error: argument of type "const void *" is incompatible with parameter of type "const double *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9327): error: argument of type "const void *" is incompatible with parameter of type "const int *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9255): error: argument of type "const void *" is incompatible with parameter of type "const double *"

/usr/lib/gcc/x86_64-linux-gnu/5/include/avx512fintrin.h(9340): error: argument of type "const void *" is incompatible with parameter of type "const long long *"

How to fix it? Thanks

Help with install

I am a novice using cmake, and am stuck on the install.

The doc says:

cd cuda_backend
cmake -D CUDA_TOOLKIT_ROOT_DIR=/path/to/cuda .
make -j8

I have installed cmake, I cd to the directory I have cloned this project (.\cuda_spatial_deform\cuda_backend), but in the second step when I replace CUDA_TOOLKIT_ROOT_DIR with path to the version of CUDA I have installed (11.2)

cmake -D CUDA_TOOLKIT_ROOT_DIR=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2

I get the following errors:

CMake Warning:
  Ignoring extra path from command line:

   Toolkit\CUDA\v11.2


CMake Error: The source directory "./cuda_spatial_deform/cuda_backend/Files/NVIDIA" does not exist.
Specify --help for usage, or press the help button on the CMake GUI.

Any help would be greatly appreciated!

数据增强后的图像是空的，全是0

CUDA版本：12.4

求教程讲解

您好，
工程写的真好，考虑出个教程或者博客仔细讲解一下每部分的实现吗？
不胜感激！

Elastic transformation for data loader

Does the library support applying the same transformation (elastic) to different 2D slices? Because we want to apply the same transformation to each slice of 3D data.

Thank you so much for your contribution!

How to ensure the consistency of the transformation of different modal images and the corresponding label

My codes:
import os
import SimpleITK as sitk
from PIL import Image
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import join, subfolders, isfile
from cuda_backend.py_api import Handle
import deform

`
def test_3D():
filenames = get_list_of_files('/home/xwl/Data/aug')
for files in filenames:
if 'LGG' in files[0]:
for file in files:
print(file)
sp = file.split('.nii.gz')

            sitk_image = sitk.ReadImage(file)
            array_image = sitk.GetArrayFromImage(sitk_image).copy()
            array_image = array_image.astype(np.float32)
            cuda_handle = Handle(array_image.shape, mode="constant")
            # cuda_handle.test()
            # cuda_handle.scale(1.2)
            np.random.seed(0)
            p = np.array([0.8, 0.2])
            is_flipx = np.random.choice([False, True], p=p.ravel())
            is_flipy = np.random.choice([False, True], p=p.ravel())
            is_flipz = np.random.choice([False, True], p=p.ravel())
            cuda_handle.flip(do_y=is_flipx, do_x=is_flipy, do_z=is_flipz)
            # cuda_handle.translate(100, 100, 20)
            # cuda_handle.rotate(0.75 * np.pi, 0.75 * np.pi, 0.75 * np.pi)
            cuda_handle.elastic(sigma=5., alpha=200., mode='constant')
            cuda_handle.end_flag()

            # correct_ret = deform.spatial_augment(array_image, mode="mirror")
            # Warm up and Unit test
            # for i in range(100):
            # if 'seg' not in sp[0].split('/')[-1].split('_'):
            #     output = cuda_handle.augment(array_image, order=1)
            # else:
            #     output = cuda_handle.augment(array_image, order=0)
            output = cuda_handle.augment(array_image, order=0)
            volOut = sitk.GetImageFromArray(output[0])
            id = sp[0].split('/')[-2]
            dirname = "/home/xwl/Data/training/preprocessed/pre_" + id
            if not os.path.exists(dirname):
                os.makedirs(dirname)
                print("dir create ok")
            if os.path.exists(dirname):
                sitk.WriteImage(volOut, dirname + "/" + "pre_" + file.split('/')[-1], True)

def get_list_of_files(base_dir):
"""
returns a list of lists containing the filenames. The outer list contains all training examples. Each entry in the
outer list is again a list pointing to the files of that training example in the following order:
T1, T1c, T2, FLAIR, segmentation
:param base_dir:
:return:
"""
list_of_lists = []
for glioma_type in ['LGG']: # ['HGG', 'LGG']:
current_directory = join(base_dir, glioma_type)
patients = subfolders(current_directory, join=False)
for p in patients:
patient_directory = join(current_directory, p)
t1_file = join(patient_directory, p + "_t1.nii.gz")
t1c_file = join(patient_directory, p + "_t1ce.nii.gz")
t2_file = join(patient_directory, p + "_t2.nii.gz")
flair_file = join(patient_directory, p + "_flair.nii.gz")
seg_file = join(patient_directory, p + "_seg.nii.gz")
this_case = [t1_file, t1c_file, t2_file, flair_file, seg_file]
assert all((isfile(i) for i in this_case)), "some file is missing for patient %s; make sure the following "
"files are there: %s" % (p, str(this_case))
list_of_lists.append(this_case)
print("Found %d patients" % len(list_of_lists))
return list_of_lists

if name == "main":

test_3D()
`

and second problem:
after runing the above codes, we can get the below errors:
CUDA error at /home/xwl/cuda_spatial_deform/cuda_backend/kernel/utils.cu:97 code=2(cudaErrorMemoryAllocation) "cudaMalloc((void **)&random, coords_size * sizeof(float))"

qsyao / cuda_spatial_deform Goto Github PK

cuda_spatial_deform's People

Contributors

Stargazers

Watchers

Forkers

cuda_spatial_deform's Issues

minimal working example for elastic_deform

verification question

Can I set which GPU to run on manually

Error in cuda 9-2

Help with install

数据增强后的图像是空的，全是0

求教程讲解

Elastic transformation for data loader

How to ensure the consistency of the transformation of different modal images and the corresponding label

if name == "main":

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