Merge branch 'main' of https://github.com/C-Achard/cellseg-annotator-test

Author: C-Achard

.gitignore

@@ -90,7 +90,7 @@ venv/
 
 ########
 #project specific
-#dataset
+#dataset, weights, old logos
 /src/napari_cellseg3d/models/dataset/
 /src/napari_cellseg3d/models/saved_weights/
 /docs/res/logo/old_logo/

docs/conf.py

@@ -136,7 +136,7 @@
 
 # The name of an image file (relative to this directory) to place at the top
 # of the sidebar.
-html_logo = "res/logo/logo_alpha.png"
+html_logo = "res/logo/logo_diag.png"
 
 # The name of an image file (within the static path) to use as favicon of the
 # docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32

docs/res/guides/training_module_guide.rst

@@ -23,9 +23,12 @@ TRAILMAP          An emulation of the `TRAILMAP project on GitHub`_ using `3DUne
 .. _3DUnet for Pytorch: https://github.com/wolny/pytorch-3dunet
 
 .. important::
-    The machine learning models used by this program require all images of a dataset to be of the same size.
-    Please ensure that all the images you are loading are of the **same size**, or to use the **"extract patches" (in augmentation tab)** with an appropriately small size
-    to ensure all images being used by the model are of a workable size.
+    | The machine learning models used by this program require all images of a dataset to be of the same size.
+    | Please ensure that all the images you are loading are of the **same size**, or to use the **"extract patches" (in augmentation tab)** with an appropriately small size to ensure all images being used by the model are of a workable size.
+
+.. important::
+    | **All image sizes used should be as close to a power of two as possible, if not a power of two.**
+    | Images are automatically padded; a 64 pixels cube will be used as is, but a 65 pixel cube will be padded up to 128 pixels, resulting in much higher memory use.
 
 The training module is comprised of several tabs.
 
@@ -41,36 +44,39 @@ The training module is comprised of several tabs.
 * Whether to use images "as is" (**requires all images to be of the same size and cubic**) or extract patches.
 
 * If you're extracting patches :
-    * The size of patches to be extracted (ideally, please use a value **close to a power of two**, such as 120 or 60.
-    * The number of samples to extract from each of your image to ensure correct size and perform data augmentation. A larger number will likely mean better performances, but longer training and larger memory usage.
+
+    * The size of patches to be extracted (ideally, please use a value **close to a power of two**, such as 120 or 60 to ensure correct size.)
+    * The number of samples to extract from each of your images. A larger number will likely mean better performances, but longer training and larger memory usage.
+
 * Whether to perform data augmentation or not (elastic deforms, intensity shifts. random flipping,etc). A rule of thumb for augmentation is :
+
     * If you're using the patch extraction method, enable it if you are using more than 10 samples per image with at least 5 images
     * If you have a large dataset and are not using patches extraction, enable it.
 
 
 3) The third contains training related parameters :
 
-* The model to use for training (see table above)
-* The loss function used for training (see table below)
-* The batch size (larger means quicker training and possibly better performance but increased memory usage)
-* The number of epochs (a possibility is to start with 60 epochs, and decrease or increase depending on performance.)
-* The epoch interval for validation (for example, if set to two, the module will use the validation dataset to evaluate the model with the dice metric every two epochs.)
+* The **model** to use for training (see table above)
+* The **loss function** used for training (see table below)
+* The **batch size** (larger means quicker training and possibly better performance but increased memory usage)
+* The **number of epochs** (a possibility is to start with 60 epochs, and decrease or increase depending on performance.)
+* The **epoch interval** for validation (for example, if set to two, the module will use the validation dataset to evaluate the model with the dice metric every two epochs.)
 
-If the dice metric is better on that validation interval, the model weights will be saved in the results folder.
+.. note::
+    If the dice metric is better on a given validation interval, the model weights will be saved in the results folder.
 
 The available loss functions are :
 
-========================  ====================================================
+========================  ================================================================================================
 Function                  Reference
-========================  ====================================================
+========================  ================================================================================================
 Dice loss                 `Dice Loss from MONAI`_ with ``sigmoid=true``
 Focal loss                `Focal Loss from MONAI`_
 Dice-Focal loss           `Dice-focal Loss from MONAI`_ with ``sigmoid=true`` and ``lambda_dice = 0.5``
 Generalized Dice loss     `Generalized dice Loss from MONAI`_ with ``sigmoid=true``
 Dice-CE loss              `Dice-CE Loss from MONAI`_ with ``sigmoid=true``
 Tversky loss              `Tversky Loss from MONAI`_ with ``sigmoid=true``
-========================  ====================================================
-
+========================  ================================================================================================
 .. _Dice Loss from MONAI: https://docs.monai.io/en/stable/losses.html#diceloss
 .. _Focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#focalloss
 .. _Dice-focal Loss from MONAI: https://docs.monai.io/en/stable/losses.html#dicefocalloss

docs/res/logo/logo_alpha.png

@@ -68,6 +68,7 @@ Then go into Plugins > napari-cellseg3d, and choose which tool to use.
 - **Review**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.
 - **Utilities**: This module allows you to use several utilities, e.g. to crop your volumes and labels, compute prediction scores or convert labels
 
+See above for links to detailed guides regarding the usage of the modules.
 
 Acknowledgments & References
 ---------------------------------------------

docs/res/logo/logo_alpha_flat.png

@@ -7,7 +7,6 @@
 
 
 def test_prepare(make_napari_viewer):
-    path_to_csv = Path(os.path.dirname(os.path.realpath(__file__)) + "/res")
     path_image = Path(
         os.path.dirname(os.path.realpath(__file__)) + "/res/test.tif"
     )
@@ -16,7 +15,7 @@ def test_prepare(make_napari_viewer):
     viewer.add_image(image)
     widget = Datamanager(viewer)
 
-    widget.prepare(path_to_csv, ".tif", "", False, False)
+    widget.prepare(path_image, ".tif", "", False, False)
 
     assert widget.filetype == ".tif"
     assert widget.as_folder == False

docs/res/logo/logo_diag.png

@@ -67,6 +67,13 @@ def test_get_padding_dim():
 
     assert pad == [2048, 32, 64]
 
+    tensor = torch.randn(65,70,80)
+    size = tensor.size()
+
+    pad = utils.get_padding_dim(size)
+
+    assert pad == [128,128,128]
+
 
 def test_normalize_x():