IOSegmentorConfig¶

class IOSegmentorConfig(input_resolutions, output_resolutions, patch_input_shape, patch_output_shape, save_resolution=None, **kwargs)[source]¶

Contain semantic segmentor input and output information.

Parameters:

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

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

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

  • patch_output_shape (numpy.ndarray, list(int)) – Shape of the largest output in (height, width).

  • save_resolution (dict) – Resolution to save all output.

  • kwargs (dict)

Examples

>>> # Defining io for a network having 1 input and 1 output at the
>>> # same resolution
>>> ioconfig = IOSegmentorConfig(
...     input_resolutions=[{"units": "baseline", "resolution": 1.0}],
...     output_resolutions=[{"units": "baseline", "resolution": 1.0}],
...     patch_input_shape=[2048, 2048],
...     patch_output_shape=[1024, 1024],
...     stride_shape=[512, 512],
... )

Examples

>>> # Defining io for a network having 3 input and 2 output
>>> # at the same resolution, the output is then merged at a
>>> # different resolution.
>>> ioconfig = IOSegmentorConfig(
...     input_resolutions=[
...         {"units": "mpp", "resolution": 0.25},
...         {"units": "mpp", "resolution": 0.50},
...         {"units": "mpp", "resolution": 0.75},
...     ],
...     output_resolutions=[
...         {"units": "mpp", "resolution": 0.25},
...         {"units": "mpp", "resolution": 0.50},
...     ],
...     patch_input_shape=[2048, 2048],
...     patch_output_shape=[1024, 1024],
...     stride_shape=[512, 512],
...     save_resolution={"units": "mpp", "resolution": 4.0},
... )

Initialize IOSegmentorConfig.

Methods

scale_to_highest

Get the scaling factor from input resolutions.

to_baseline

Return a new config object converted to baseline form.

Attributes

input_resolutions

output_resolutions

static scale_to_highest(resolutions, units)[source]¶

Get the scaling factor from input resolutions.

This will convert resolutions to a scaling factor with respect to the highest resolution found in the input resolutions list.

Parameters:

  • resolutions (list) – A list of resolutions where one is defined as {‘resolution’: value, ‘unit’: value}

  • units (Units) – Units that the resolutions are at.

Returns:

A 1D array of scaling factors having the same length as resolutions

Return type:

numpy.ndarray

to_baseline()[source]¶

Return a new config object converted to baseline form.

This will return a new IOSegmentorConfig where resolutions have been converted to baseline format with the highest possible resolution found in both input and output as reference.

Parameters:

self (IOSegmentorConfig)

Return type:

IOSegmentorConfig