mapreader.process.occlusion_analysis

Classes

A class for carrying out occlusion analysis on patches.

class mapreader.process.occlusion_analysis.OcclusionAnalyzer(patch_df, model, transform='default', delimiter=',', device='default')

A class for carrying out occlusion analysis on patches.

Parameters:

patch_df (pd.DataFrame | gpd.GeoDataFrame | str | pathlib.Path) – The DataFrame containing patches and predictions.
model (str or nn.Module) – The PyTorch model to add to the object. If a string, this should be the path to a model checkpoint.
transform (str or callable) – The transform to apply to the patches. Options of “default” or a torchvision transform. Default transform
delimiter (str) – The delimiter used in the patch_df csv file. By default, “,”.
device (str) – The device to use. By default, “default”.

add_loss_fn(loss_fn='cross entropy')

Add a loss function to the object.

Parameters:: loss_fn (str or torch.nn.modules.loss._Loss) – The loss function to use. Can be a string or a torch.nn loss function. Accepted string values are “cross entropy” or “ce” (cross-entropy), “bce” (binary cross-entropy) and “mse” (mean squared error). By default, “cross entropy” is used.
Return type:: None

run_occlusion(label, sample_size=10, save=False, path_save='./occlusion_analysis/', block_size=14)

Run occlusion analysis on a sample of patches for a given label.

Parameters:

label (str) – The label to run the analysis on.
sample_size (int) – The number of patches to run the analysis on. By default, 10.
save (bool) – Whether to save the occlusion analysis images. By default, False.
path_save (str) – The path to save the occlusion analysis images to. By default, “./occlusion_analysis/”.
block_size (int) – The size of the occlusion block. By default, 14.