CellVitPanoptic
Bases: BaseModelPanoptic
Source code in src/histolytics/models/cellvit_panoptic.py
__init__ ¶
__init__(n_nuc_classes: int, n_tissue_classes: int, enc_name: str = 'samvit_base_patch16', enc_pretrain: bool = True, enc_freeze: bool = False, device: device = torch.device('cuda'), model_kwargs: Dict[str, Any] = {}) -> None
CellVitPanoptic model for panoptic segmentation of nuclei and tissues.
Note
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n_nuc_classes
|
int
|
Number of nuclei type classes. |
required |
n_tissue_classes
|
int
|
Number of tissue type classes. |
required |
enc_name
|
str
|
Name of the pytorch-image-models encoder. |
'samvit_base_patch16'
|
enc_pretrain
|
bool
|
Whether to use pretrained weights in the encoder. |
True
|
enc_freeze
|
bool
|
Freeze encoder weights for training. |
False
|
device
|
device
|
Device to run the model on. |
device('cuda')
|
model_kwargs
|
dict
|
Additional keyword arguments for the model. |
{}
|
Source code in src/histolytics/models/cellvit_panoptic.py
set_inference_mode ¶
Set model to inference mode.
Source code in src/histolytics/models/cellvit_panoptic.py
from_pretrained
classmethod
¶
from_pretrained(weights: Union[str, Path], device: device = torch.device('cuda'), model_kwargs: Dict[str, Any] = {})
Load the model from pretrained weights.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name
|
str
|
Name of the pretrained model. |
required |
device
|
device
|
Device to run the model on. |
device('cuda')
|
model_kwargs
|
Dict[str, Any]
|
Additional arguments for the model. |
{}
|
Examples:
Source code in src/histolytics/models/_base_model.py
predict ¶
predict(x: Union[Tensor, ndarray, Image], *, use_sliding_win: bool = False, window_size: Tuple[int, int] = None, stride: int = None) -> Dict[str, Union[SoftSemanticOutput, SoftInstanceOutput]]
Predict the input image or image batch.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
Union[Tensor, ndarray, Image]
|
Input image (H, W, C) or input image batch (B, C, H, W). |
required |
use_sliding_win
|
bool
|
Whether to use sliding window for prediction. |
False
|
window_size
|
Tuple[int, int]
|
The height and width of the sliding window. If |
None
|
stride
|
int
|
The stride for the sliding window. If |
None
|
Returns:
Type | Description |
---|---|
Dict[str, Union[SoftSemanticOutput, SoftInstanceOutput]]
|
Dict[str, Union[SoftSemanticOutput, SoftInstanceOutput]]: Dictionary of soft outputs:
|
Examples:
>>> my_model.set_inference_mode()
>>> # with sliding window if image is large
>>> x = my_model.predict(x=image, use_sliding_win=True, window_size=(256, 256), stride=128)
>>> # without sliding window if image is small enough
>>> x = my_model.predict(x=image, use_sliding_win=False)
Source code in src/histolytics/models/_base_model.py
post_process ¶
post_process(x: Dict[str, Union[SoftSemanticOutput, SoftInstanceOutput]], *, use_async_postproc: bool = True, start_method: str = 'threading', n_jobs: int = 4, save_paths_nuc: List[Union[Path, str]] = None, save_paths_cyto: List[Union[Path, str]] = None, save_paths_tissue: List[Union[Path, str]] = None, coords: List[Tuple[int, int, int, int]] = None, class_dict_nuc: Dict[int, str] = None, class_dict_cyto: Dict[int, str] = None, class_dict_tissue: Dict[int, str] = None) -> Dict[str, List[np.ndarray]]
Post-process the output of the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
Dict[str, Union[SoftSemanticOutput, SoftInstanceOutput]]
|
The output of the .predict() method. |
required |
use_async_postproc
|
bool
|
Whether to use async post-processing. Can give some run-time benefits. |
True
|
start_method
|
str
|
The start method. One of: "threading", "fork", "spawn". See mpire docs. |
'threading'
|
n_jobs
|
int
|
The number of workers for the post-processing. |
4
|
save_paths_nuc
|
List[Union[Path, str]]
|
The paths to save the panlei masks. If None, the masks are not saved. |
None
|
save_paths_cyto
|
List[Union[Path, str]]
|
The paths to save the cytoplasm masks. If None, the masks are not saved. |
None
|
save_paths_tissue
|
List[Union[Path, str]]
|
The paths to save the tissue masks. If None, the masks are not saved. |
None
|
coords
|
List[Tuple[int, int, int, int]]
|
The XYWH coordinates of the image patch. If not None, the coordinates are saved in the filenames of outputs. |
None
|
class_dict_nuc
|
Dict[int, str]
|
The dictionary of panlei classes. E.g. {0: "bg", 1: "neoplastic"} |
None
|
class_dict_cyto
|
Dict[int, str]
|
The dictionary of cytoplasm classes. E.g. {0: "bg", 1: "macrophage_cyto"} |
None
|
class_dict_tissue
|
Dict[int, str]
|
The dictionary of tissue classes. E.g. {0: "bg", 1: "stroma", 2: "tumor"} |
None
|
Returns:
Type | Description |
---|---|
Dict[str, List[ndarray]]
|
Dict[str, List[np.ndarray]]: Dictionary of post-processed outputs:
|
Examples:
>>> my_model.set_inference_mode()
>>> x = my_model.predict(x=image, use_sliding_win=False)
>>> x = my_model.post_process(
... x,
... use_async_postproc=True,
... start_method="threading",
... n_jobs=4,
... )
Source code in src/histolytics/models/_base_model.py
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