IOPatchPredictorConfig¶

class IOPatchPredictorConfig(input_resolutions, patch_input_shape, stride_shape=None, output_resolutions=<factory>)[source]¶

Contains patch predictor input and output information.

Parameters:

  • input_resolutions (list(dict)) – Resolution of each input head of model inference, must be in the same order as target model.forward().

  • patch_input_shape (numpy.ndarray, list(int), tuple(int)) – Shape of the largest input in (height, width).

  • stride_shape (numpy.ndarray, list(int), tuple(int)) – Stride in (x, y) direction for patch extraction.

  • output_resolutions (list(dict)) – Resolution of each output head from model inference, must be in the same order as target model.infer_batch().

input_resolutions¶

Resolution of each input head of model inference, must be in the same order as target model.forward().

Type:

list(dict)

patch_input_shape¶

Shape of the largest input in (height, width).

Type:

numpy.ndarray, list(int), tuple(int)

stride_shape¶

Stride in (x, y) direction for patch extraction.

Type:

numpy.ndarray, list(int), tuple(int)

output_resolutions¶

Resolution of each output head from model inference, must be in the same order as target model.infer_batch().

Type:

list(dict)

highest_input_resolution¶

Highest resolution to process the image based on input and output resolutions. This helps to read the image at the optimal resolution and improves performance.

Type:

dict

Examples

>>> # Defining io for a patch predictor network
>>> ioconfig = IOPatchPredictorConfig(
...     input_resolutions=[{"units": "mpp", "resolution": 0.5}],
...     output_resolutions=[{"units": "mpp", "resolution": 0.5}],
...     patch_input_shape=(224, 224),
...     stride_shape=(224, 224),
... )

Methods

Attributes

input_resolutions

patch_input_shape

output_resolutions