Function pca_visualize

fourm/utils/plotting_utils.py:65–78

Visualizes a feature map using PCA. Args: features (torch.Tensor): CxHxW feature map to visualize. n_components (int): Number of PCA components to use.

(features, n_components=3)

    return img.detach().cpu().numpy()

def pca_visualize(features, n_components=3):
    """
    Visualizes a feature map using PCA.

    Args:
        features (torch.Tensor): CxHxW feature map to visualize.
        n_components (int): Number of PCA components to use.
    """
    C, H, W = features.shape
    features_flat = rearrange(features.float(), 'c h w -> (h w) c').detach().cpu().numpy()
    pca = PCA(n_components=n_components)
    img_pca = rearrange(pca.fit_transform(features_flat), '(h w) c -> h w c', h=H, w=W)
    img_pca = (img_pca - img_pca.min()) / (img_pca.max() - img_pca.min())
    return img_pca

def np_squeeze(array, axis=0):
    """