BioExp.spatial package

Submodules

BioExp.spatial.ablation module

class BioExp.spatial.ablation.Ablate(model, weights_pth, metric, layer_name, test_image, gt, classes, nclasses=4, image_name=None)

Bases: object

A class for conducting an ablation study on a trained keras model instance

ablate_filters(filters_to_ablate=None, concept='random', step=None, save_path=None, verbose=1)

Drops individual weights from the model, makes the prediction for the test image, and calculates the difference in the evaluation metric as compared to the non- ablated case. For example, for a layer with a weight matrix of shape 3x3x64, individual 3x3 matrices are zeroed out at the interval given by the step argument.

Arguments: step: The interval at which to drop weights Outputs: A dataframe containing the importance scores for each individual weight matrix in the layer

BioExp.spatial.dissection module

class BioExp.spatial.dissection.Dissector(model, layer_name, seq=None)

Bases: object

Network Dissection analysis

model : keras model initialized with trained weights layer_name : intermediate layer name which needs to be analysed

apply_threshold(image, threshold_maps, nfeatures=None, save_path=None, post_process_threshold=80, ROI=None)

apply thresholded mask and saves the feature maps for specific iamge

image: test image (Hx W xC) thresholded_maps: threshold maps used for dissection nfeatures : number of features to visualize

all if None

save_path : if None just displayes image else saves feature maps in

given path

post_process_threshold: threshold for postprocessing cc analysis ROI : region of interest mask in a given image

get_threshold_maps(dataset_path, save_path, percentile, loader=None)

Estimates threshold maps for given percentile value

dataset_path: input dataset path save_path : path to save feature maps

if fmaps exists already it directly loads

percentile : value used for thresholding obtained feature maps

range: (0, 100)

quantify_gt_features(image, gt, threshold_maps, nclasses, nfeatures, save_path, save_fmaps=False, post_process_threshold=80, ROI=None)

Quatify the learnt internal concepts by a network, only valid for segmentation networks

image : image (H x W x C) gt : image (H x W) threshold_maps : threshold maps used for dissection nclasses : number of classes nfeatures : number of feature maps to consider save_path : path to save csv with score for each featurs save_fmaps: saves images with fmap overlap post_process_threshold: threshold for postprocessing cc analysis ROI : region of interest mask in a given image

BioExp.spatial.flow module

Module contents