unify visualisation

Signed-off-by: Richard Brown <[email protected]>

docs/source/conf.py

@@ -43,7 +43,7 @@
     "config",
     "handlers",
     "losses",
-    "visualize",
+    "visualise",
     "utils",
     "inferers",
     "optimizers",

docs/source/highlights.md

@@ -21,7 +21,7 @@ The rest of this page provides more details for each module.
 * [Optimizers](#optimizers)
 * [Network architectures](#network-architectures)
 * [Evaluation](#evaluation)
-* [Visualization](#visualization)
+* [Visualisation](#visualisation)
 * [Result writing](#result-writing)
 * [Workflows](#workflows)
 * [Research](#research)
@@ -111,7 +111,7 @@ MONAI also provides post-processing transforms for handling the model outputs. C
 - Removing segmentation noise based on Connected Component Analysis, as below figure (c).
 - Extracting contour of segmentation result, which can be used to map to original image and evaluate the model, as below figure (d) and (e).
 
-After applying the post-processing transforms, it's easier to compute metrics, save model output into files or visualize data in the TensorBoard. [Post transforms tutorial](https://github.com/Project-MONAI/tutorials/blob/master/modules/post_transforms.ipynb) shows an example with several main post transforms.
+After applying the post-processing transforms, it's easier to compute metrics, save model output into files or visualise data in the TensorBoard. [Post transforms tutorial](https://github.com/Project-MONAI/tutorials/blob/master/modules/post_transforms.ipynb) shows an example with several main post transforms.
 ![image](../images/post_transforms.png)
 
 ### 9. Integrate third-party transforms
@@ -237,10 +237,10 @@ Various useful evaluation metrics have been used to measure the quality of medic
 
 For example, `Mean Dice` score can be used for segmentation tasks, and the area under the ROC curve(`ROCAUC`) for classification tasks. We continue to integrate more options.
 
-## Visualization
-Beyond the simple point and curve plotting, MONAI provides intuitive interfaces to visualize multidimensional data as GIF animations in TensorBoard. This could provide a quick qualitative assessment of the model by visualizing, for example, the volumetric inputs, segmentation maps, and intermediate feature maps. A runnable example with visualization is available at [UNet training example](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/torch/unet_training_dict.py).
+## Visualisation
+Beyond the simple point and curve plotting, MONAI provides intuitive interfaces to visualise multidimensional data as GIF animations in TensorBoard. This could provide a quick qualitative assessment of the model by visualising, for example, the volumetric inputs, segmentation maps, and intermediate feature maps. A runnable example with visualisation is available at [UNet training example](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/torch/unet_training_dict.py).
 
-And to visualize the class activation mapping for a trained classification model, MONAI provides CAM, GradCAM, GradCAM++ APIs for both 2D and 3D models:
+And to visualise the class activation mapping for a trained classification model, MONAI provides CAM, GradCAM, GradCAM++ APIs for both 2D and 3D models:
 
 ![image](../images/cam.png)
 

docs/source/index.rst

@@ -61,7 +61,7 @@ Technical documentation is available at `docs.monai.io <https://docs.monai.io>`_
    engines
    inferers
    handlers
-   visualize
+   visualise
    utils
 
 .. toctree::

docs/source/metrics.rst

@@ -37,7 +37,3 @@ Metrics
 
 .. autoclass:: SurfaceDistanceMetric
     :members:
-
-`Occlusion sensitivity`
------------------------
-.. autofunction:: compute_occlusion_sensitivity

docs/source/visualize.rst → docs/source/visualise.rst

@@ -1,20 +1,20 @@
 :github_url: https://github.com/Project-MONAI/MONAI
 
-.. _visualize:
+.. _visualise:
 
-Visualizations
+Visualisations
 ==============
 
-.. currentmodule:: monai.visualize
+.. currentmodule:: monai.visualise
 
 Tensorboard visuals
 -------------------
 
-.. automodule:: monai.visualize.img2tensorboard
+.. automodule:: monai.visualise.img2tensorboard
   :members:
 
 Class activation map
 --------------------
 
-.. automodule:: monai.visualize.class_activation_maps
+.. automodule:: monai.visualise.class_activation_maps
   :members:

monai/README.md

@@ -27,4 +27,4 @@
 * **utils**: generic utilities intended to be implemented in pure Python or using Numpy,
 and not with Pytorch, such as namespace aliasing, auto module loading.
 
-* **visualize**: utilities for data visualization.
+* **visualise**: utilities for data visualisation.

monai/handlers/tensorboard_handlers.py

@@ -16,7 +16,7 @@
 import torch
 
 from monai.utils import exact_version, is_scalar, optional_import
-from monai.visualize import plot_2d_or_3d_image
+from monai.visualise import plot_2d_or_3d_image
 
 Events, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Events")
 if TYPE_CHECKING:

monai/metrics/__init__.py

@@ -12,7 +12,6 @@
 from .confusion_matrix import ConfusionMatrixMetric, compute_confusion_matrix_metric, get_confusion_matrix
 from .hausdorff_distance import *
 from .meandice import DiceMetric, compute_meandice
-from .occlusion_sensitivity import compute_occlusion_sensitivity
 from .rocauc import compute_roc_auc
 from .surface_distance import SurfaceDistanceMetric, compute_average_surface_distance
 from .utils import *

monai/visualize/__init__.py → monai/visualise/__init__.py

@@ -9,5 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .visualiser import ModelWithHooks, NetVisualiser, default_normalizer, default_upsampler  # isort:skip
 from .class_activation_maps import *
 from .img2tensorboard import *
+from .occlusion_sensitivity import OcclusionSensitivity