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1378 Add output_dtype to NiftiSaver and SegmentationSaver #1394

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merged 2 commits into from
Dec 28, 2020
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1378 Add output_dtype to NiftiSaver and SegmentationSaver #1394

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dc2381b
Merge pull request #2 from Project-MONAI/master
Nic-Ma Dec 23, 2020
e85c5f4
[DLMED] add output_dtype
Nic-Ma Dec 23, 2020
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4 changes: 4 additions & 0 deletions monai/data/nifti_saver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,7 @@ def __init__(
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
align_corners: bool = False,
dtype: Optional[np.dtype] = np.float64,
output_dtype: Optional[np.dtype] = np.float32,
) -> None:
"""
Args:
Expand All @@ -57,6 +58,7 @@ def __init__(
dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
If None, use the data type of input data. To be compatible with other modules,
the output data type is always ``np.float32``.
output_dtype: data type for saving data. Defaults to ``np.float32``.
"""
self.output_dir = output_dir
self.output_postfix = output_postfix
Expand All @@ -66,6 +68,7 @@ def __init__(
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
self.align_corners = align_corners
self.dtype = dtype
self.output_dtype = output_dtype
self._data_index = 0

def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:
Expand Down Expand Up @@ -118,6 +121,7 @@ def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict]
padding_mode=self.padding_mode,
align_corners=self.align_corners,
dtype=self.dtype,
output_dtype=self.output_dtype,
)

def save_batch(self, batch_data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None:
Expand Down
10 changes: 7 additions & 3 deletions monai/handlers/segmentation_saver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,8 @@ def __init__(
mode: Union[GridSampleMode, InterpolateMode, str] = "nearest",
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
scale: Optional[int] = None,
dtype: Optional[np.dtype] = None,
dtype: Optional[np.dtype] = np.float64,
output_dtype: Optional[np.dtype] = np.float32,
batch_transform: Callable = lambda x: x,
output_transform: Callable = lambda x: x,
name: Optional[str] = None,
Expand Down Expand Up @@ -69,8 +70,10 @@ def __init__(
scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling
[0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.
It's used for PNG format only.
dtype: convert the image data to save to this data type.
If None, keep the original type of data. It's used for Nifti format only.
dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
If None, use the data type of input data. To be compatible with other modules,
the output data type is always ``np.float32``, it's used for Nifti format only.
output_dtype: data type for saving data. Defaults to ``np.float32``, it's used for Nifti format only.
batch_transform: a callable that is used to transform the
ignite.engine.batch into expected format to extract the meta_data dictionary.
output_transform: a callable that is used to transform the
Expand All @@ -90,6 +93,7 @@ def __init__(
mode=GridSampleMode(mode),
padding_mode=padding_mode,
dtype=dtype,
output_dtype=output_dtype,
)
elif output_ext == ".png":
self.saver = PNGSaver(
Expand Down