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Dev: refactorization and added docstring
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skaliy committed Jul 12, 2023
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9 changes: 8 additions & 1 deletion CONTRIBUTING.md
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# How to contribute
fastMONAI follows the same contribution policy as fastai: https://github.com/fastai/nbdev/blob/master/CONTRIBUTING.md
For any issues related to the source code, please open an issue in the corresponding GitHub repository. Contributions to the code or the model are welcome and should be proposed through a pull request.

## How to get started
Install the git hooks that run automatic scripts during each commit and merge to strip the notebooks of superfluous metadata (and avoid merge conflicts). After cloning the repository, run the following command inside it:
nbdev_install_hooks

1. pip install -e 'fastMONAI[dev]'
2. nbdev_install_hooks
2 changes: 1 addition & 1 deletion fastMONAI/__init__.py
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__version__ = "0.3.1"
__version__ = "0.3.2"
26 changes: 6 additions & 20 deletions fastMONAI/_modidx.py
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Expand Up @@ -29,10 +29,10 @@
'fastMONAI/external_data.py'),
'fastMONAI.external_data._process_ixi_xls': ( 'external_data.html#_process_ixi_xls',
'fastMONAI/external_data.py'),
'fastMONAI.external_data._process_nodule_img': ( 'external_data.html#_process_nodule_img',
'fastMONAI/external_data.py'),
'fastMONAI.external_data.download_NoduleMNIST3D': ( 'external_data.html#download_nodulemnist3d',
'fastMONAI/external_data.py'),
'fastMONAI.external_data._process_medmnist_img': ( 'external_data.html#_process_medmnist_img',
'fastMONAI/external_data.py'),
'fastMONAI.external_data.download_and_process_MedMNIST3D': ( 'external_data.html#download_and_process_medmnist3d',
'fastMONAI/external_data.py'),
'fastMONAI.external_data.download_example_spine_data': ( 'external_data.html#download_example_spine_data',
'fastMONAI/external_data.py'),
'fastMONAI.external_data.download_ixi_data': ( 'external_data.html#download_ixi_data',
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'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.ZNormalization.__init__': ( 'vision_augment.html#znormalization.__init__',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.ZNormalization._do_z_normalization': ( 'vision_augment.html#znormalization._do_z_normalization',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.ZNormalization.encodes': ( 'vision_augment.html#znormalization.encodes',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_rand_biasfield': ( 'vision_augment.html#_do_rand_biasfield',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_rand_blur': ( 'vision_augment.html#_do_rand_blur',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_rand_gamma': ( 'vision_augment.html#_do_rand_gamma',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_rand_ghosting': ( 'vision_augment.html#_do_rand_ghosting',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_rand_motion': ( 'vision_augment.html#_do_rand_motion',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_rand_noise': ( 'vision_augment.html#_do_rand_noise',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_rand_spike': ( 'vision_augment.html#_do_rand_spike',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation._do_z_normalization': ( 'vision_augment.html#_do_z_normalization',
'fastMONAI/vision_augmentation.py'),
'fastMONAI.vision_augmentation.do_pad_or_crop': ( 'vision_augment.html#do_pad_or_crop',
'fastMONAI/vision_augmentation.py')},
'fastMONAI.vision_core': { 'fastMONAI.vision_core.MedBase': ('vision_core.html#medbase', 'fastMONAI/vision_core.py'),
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108 changes: 58 additions & 50 deletions fastMONAI/dataset_info.py
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import glob

# %% ../nbs/08_dataset_info.ipynb 4
class MedDataset():
'''A class to extract and present information about the dataset.'''

def __init__(self, path=None, # Path to the image folder
postfix:str='', # Specify the file type if there are different files in the folder
img_list:list=None, # Alternatively pass in a list with image paths
reorder:bool=False, # Whether to reorder the data to be closest to canonical (RAS+) orientation
dtype:(MedImage, MedMask)=MedImage, # Load data as datatype
max_workers:int=1 # The number of worker threads
):
'''Constructs all the necessary attributes for the MedDataset object.'''
class MedDataset:
"""A class to extract and present information about the dataset."""

def __init__(self, path=None, postfix: str = '', img_list: list = None,
reorder: bool = False, dtype: (MedImage, MedMask) = MedImage,
max_workers: int = 1):
"""Constructs MedDataset object.
Args:
path (str, optional): Path to the image folder.
postfix (str, optional): Specify the file type if there are different files in the folder.
img_list (List[str], optional): Alternatively, pass in a list with image paths.
reorder (bool, optional): Whether to reorder the data to be closest to canonical (RAS+) orientation.
dtype (Union[MedImage, MedMask], optional): Load data as datatype. Default is MedImage.
max_workers (int, optional): The number of worker threads. Default is 1.
"""

self.path = path
self.postfix = postfix
self.img_list = img_list
Expand All @@ -35,48 +40,43 @@ def __init__(self, path=None, # Path to the image folder
self.df = self._create_data_frame()

def _create_data_frame(self):
'''Private method that returns a dataframe with information about the dataset
Returns:
DataFrame: A DataFrame with information about the dataset.
'''
"""Private method that returns a dataframe with information about the dataset."""

if self.path:
self.img_list = glob.glob(f'{self.path}/*{self.postfix}*')
if not self.img_list: print('Could not find images. Check the image path')

with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
data_info_dict = list(executor.map(self._get_data_info, self.img_list))

df = pd.DataFrame(data_info_dict)
if df.orientation.nunique() > 1: print('The volumes in this dataset have different orientations. Recommended to pass in the argument reorder=True when creating a MedDataset object for this dataset')

if df.orientation.nunique() > 1:
print('The volumes in this dataset have different orientations. '
'Recommended to pass in the argument reorder=True when creating a MedDataset object for this dataset')

return df

def summary(self):
'''Summary DataFrame of the dataset with example path for similar data.'''

"""Summary DataFrame of the dataset with example path for similar data."""
columns = ['dim_0', 'dim_1', 'dim_2', 'voxel_0', 'voxel_1', 'voxel_2', 'orientation']
return self.df.groupby(columns,as_index=False).agg(example_path=('path', 'min'), total=('path', 'size')).sort_values('total', ascending=False)

return self.df.groupby(columns, as_index=False).agg(
example_path=('path', 'min'), total=('path', 'size')
).sort_values('total', ascending=False)

def suggestion(self):
'''Voxel value that appears most often in dim_0, dim_1 and dim_2, and wheter the data should be reoriented.'''
"""Voxel value that appears most often in dim_0, dim_1 and dim_2, and whether the data should be reoriented."""

resample = [self.df.voxel_0.mode()[0], self.df.voxel_1.mode()[0], self.df.voxel_2.mode()[0]]

return resample, self.reorder

def _get_data_info(self, fn:str):
'''Private method to collect information about an image file.
def _get_data_info(self, fn: str):
"""Private method to collect information about an image file."""
_, o, _ = med_img_reader(fn, dtype=self.dtype, reorder=self.reorder, only_tensor=False)

Args:
fn: Image file path.
Returns:
dict: A dictionary with information about the image file
'''

_,o,_ = med_img_reader(fn, dtype=self.dtype, reorder=self.reorder, only_tensor=False)

info_dict = {'path': fn, 'dim_0': o.shape[1], 'dim_1': o.shape[2], 'dim_2' :o.shape[3],
info_dict = {'path': fn, 'dim_0': o.shape[1], 'dim_1': o.shape[2], 'dim_2': o.shape[3],
'voxel_0': round(o.spacing[0], 4), 'voxel_1': round(o.spacing[1], 4), 'voxel_2': round(o.spacing[2], 4),
'orientation': f'{"".join(o.orientation)}+'}

Expand All @@ -87,28 +87,36 @@ def _get_data_info(self, fn:str):

return info_dict

def get_largest_img_size(self,
resample:list=None # A list with voxel spacing [dim_0, dim_1, dim_2]
) -> list:
'''Get the largest image size in the dataset.'''
dims = None
def get_largest_img_size(self, resample: list = None) -> list:
"""Get the largest image size in the dataset."""

if resample is not None:

dims = None

if resample is not None:
org_voxels = self.df[["voxel_0", "voxel_1", 'voxel_2']].values
org_dims = self.df[["dim_0", "dim_1", 'dim_2']].values

ratio = org_voxels/resample
new_dims = (org_dims * ratio).T
dims = [new_dims[0].max().round(), new_dims[1].max().round(), new_dims[2].max().round()]

else: dims = [df.dim_0.max(), df.dim_1.max(), df.dim_2.max()]


else:
dims = [df.dim_0.max(), df.dim_1.max(), df.dim_2.max()]

return dims

# %% ../nbs/08_dataset_info.ipynb 5
def get_class_weights(train_labels:(np.array, list), class_weight='balanced'):
'''calculate class weights.'''
def get_class_weights(labels: (np.array, list), class_weight: str = 'balanced') -> torch.Tensor:
"""Calculates and returns the class weights.
Args:
labels: An array or list of class labels for each instance in the dataset.
class_weight: Defaults to 'balanced'.
Returns:
A tensor of class weights.
"""

class_weights = compute_class_weight(class_weight=class_weight, classes=np.unique(labels), y=labels)

class_weights = compute_class_weight(class_weight=class_weight, classes=np.unique(train_labels), y=train_labels)
return torch.Tensor(class_weights)
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