ignore reqs+warning+window inference+docs

Author: C-Achard

.gitignore

@@ -90,7 +90,8 @@ venv/
 
 ########
 #project specific
-#dataset, weights, old logos
+#dataset, weights, old logos, requirements
 /napari_cellseg3d/models/dataset/
 /napari_cellseg3d/models/saved_weights/
 /docs/res/logo/old_logo/
+/reqs/

docs/res/guides/inference_module_guide.rst

@@ -42,6 +42,14 @@ Interface and functionalities
   | You may also choose to **load custom weights** rather than the pre-trained ones, simply ensure they are **compatible** (e.g. produced from the training module for the same model)
 
 
+* **Inference parameters** :
+
+  | You can choose to use inference on the whole image at once, which generally yields better performance at the cost of more memory,
+  | or you can use a specific window size to run inference on smaller chunks one by one, for lower memory usage.
+  | You can also choose to keep the dataset in the RAM rather than the VRAM (cpu vs cuda device) to avoid running out of VRAM
+  | if you have several images.
+
+
 * **Anisotropy** :
 
   | If you want to see your results without **anisotropy** when you have anisotropic images, you can specify that you have anisotropic data

napari_cellseg3d/interface.py

@@ -1,3 +1,5 @@
+from typing import Union
+
 from qtpy.QtCore import Qt
 from qtpy.QtCore import QUrl
 from qtpy.QtGui import QDesktopServices
@@ -62,7 +64,7 @@ def add_blank(widget, layout):
 
 def open_file_dialog(
     widget,
-    possible_paths=[""],
+    possible_paths: list = [""],
     load_as_folder: bool = False,
     filetype: str = "Image file (*.tif *.tiff)",
 ):
@@ -159,7 +161,7 @@ def make_n_spinboxes(
     parent=None,
     double=False,
     fixed=True,
-):
+) -> Union[list, QWidget]:
     """
 
     Args:
@@ -303,7 +305,7 @@ def make_combobox(
     """Creates a dropdown menu with a title and adds specified entries to it
 
     Args:
-        entries array(str): Entries to add to the dropdown menu. Defaults to None, no entries if None
+        entries (array(str)): Entries to add to the dropdown menu. Defaults to None, no entries if None
         parent (QWidget): parent QWidget to add dropdown menu to. Defaults to None, no parent is set if None
         label (str) : if not None, creates a QLabel with the contents of 'label', and returns the label as well
         fixed (bool): if True, will set the size policy of the dropdown menu to Fixed in h and w. Defaults to True.
@@ -367,14 +369,22 @@ def make_checkbox(
 
 
 def combine_blocks(
-    second, first, min_spacing=0, horizontal=True, l=11, t=3, r=11, b=11
+    right_or_below,
+    left_or_above,
+    min_spacing=0,
+    horizontal=True,
+    l=11,
+    t=3,
+    r=11,
+    b=11,
 ):
-    """Combines two QWidget objects and puts them side by side (label on the left and button on the right)
+    """Combines two QWidget objects and puts them side by side (first on the left/top and second on the right/bottom depending on "horizontal")
+       Weird argument names due the initial implementation of it.  # TODO maybe fix arg names
 
     Args:
-        horizontal (bool): whether to stack widgets laterally or horizontally
-        second (QWidget): Second widget, to be displayed right/below of the label
-        first (QWidget): First widget, to be added on the left/above of button
+        horizontal (bool): whether to stack widgets vertically (False) or horizontally (True)
+        left_or_above (QWidget): First widget, to be added on the left/above of "second"
+        right_or_below (QWidget): Second widget, to be displayed right/below of "first"
         min_spacing (int): Minimum spacing between the two widgets (from the start of label to the start of button)
 
     Returns:
@@ -407,9 +417,9 @@ def combine_blocks(
     # temp_layout.setColumnMinimumWidth(1,100)
     # temp_layout.setSizeConstraint(QLayout.SetMinAndMaxSize)
 
-    temp_layout.addWidget(first, r1, c1)  # , alignment=LEFT_AL)
+    temp_layout.addWidget(left_or_above, r1, c1)  # , alignment=LEFT_AL)
     # temp_layout.addStretch(100)
-    temp_layout.addWidget(second, r2, c2)  # , alignment=LEFT_AL)
+    temp_layout.addWidget(right_or_below, r2, c2)  # , alignment=LEFT_AL)
     temp_widget.setLayout(temp_layout)
     return temp_widget
 

napari_cellseg3d/model_workers.py

@@ -85,6 +85,9 @@ def __init__(
         filetype,
         transforms,
         instance,
+        use_window,
+        window_infer_size,
+        keep_on_cpu,
     ):
         """Initializes a worker for inference with the arguments needed by the :py:func:`~inference` function.
 
@@ -103,7 +106,13 @@ def __init__(
 
             * transforms: a dict containing transforms to perform at various times.
 
-            * instance : a dict containing parameters regarding instance segmentation
+            * instance: a dict containing parameters regarding instance segmentation
+
+            * use_window: use window inference with specific size or whole image
+
+            * window_infer_size: size of window if use_window is True
+
+            * keep_on_cpu: keep images on CPU or no
 
         Note: See :py:func:`~self.inference`
         """
@@ -121,6 +130,9 @@ def __init__(
         self.filetype = filetype
         self.transforms = transforms
         self.instance_params = instance
+        self.use_window = use_window
+        self.window_infer_size = window_infer_size
+        self.keep_on_cpu = keep_on_cpu
 
         """These attributes are all arguments of :py:func:~inference, please see that for reference"""
 
@@ -149,9 +161,25 @@ def log_parameters(self):
                 f"Thresholding is enabled at {self.transforms['thresh'][1]}"
             )
 
+        if self.use_window:
+            status = "enabled"
+        else:
+            status="disabled"
+
+        self.log(f"Window inference is {status}")
+
+        if self.keep_on_cpu:
+            self.log(f"Dataset loaded to CPU")
+        else:
+            self.log(f"Dataset loaded on {self.device}")
+
+        if self.instance_params["do_instance"]:
+            # TODO move instance seg
+            self.log(f"Instance segmentation enabled")
+            # self.log(f"")
+
     def inference(self):
         """
-
         Requires:
             * device: cuda or cpu device to use for torch
 
@@ -167,6 +195,12 @@ def inference(self):
 
             * transforms: a dict containing transforms to perform at various times.
 
+            * use_window: use window inference with specific size or whole image
+
+            * window_infer_size: size of window if use_window is True
+
+            * keep_on_cpu: keep images on CPU or no
+
         Yields:
             dict: contains :
                 * "image_id" : index of the returned image
@@ -215,8 +249,8 @@ def inference(self):
 
         model.to(self.device)
 
-        print("FILEPATHS PRINT")
-        print(self.images_filepaths)
+        # print("FILEPATHS PRINT")
+        # print(self.images_filepaths)
 
         load_transforms = Compose(
             [
@@ -272,18 +306,31 @@ def inference(self):
 
                 inputs = inf_data["image"]
                 # print(inputs.shape)
-                inputs = inputs.to(self.device)
+                #  TODO figure out sliding window device
+                inputs = inputs.to("cpu")
 
                 model_output = lambda inputs: post_process_transforms(
                     self.model_dict["class"].get_output(model, inputs)
                 )
+                # TODO add more params
+
+                if self.keep_on_cpu:
+                    dataset_device = "cpu"
+                else:
+                    dataset_device = self.device
+
+                if self.use_window:
+                    window_size = self.window_infer_size
+                else:
+                    window_size = None
 
                 outputs = sliding_window_inference(
                     inputs,
-                    roi_size=None,
+                    roi_size=window_size,
                     sw_batch_size=1,
                     predictor=model_output,
-                    device=self.device,
+                    sw_device=self.device,
+                    device=dataset_device,
                 )
 
                 out = outputs.detach().cpu()

napari_cellseg3d/plugin_convert.py

@@ -83,8 +83,8 @@ def build(self):
         w, layout = ui.make_container_widget()
 
         results_widget = ui.combine_blocks(
-            second=self.btn_result_path,
-            first=self.lbl_result_path,
+            right_or_below=self.btn_result_path,
+            left_or_above=self.lbl_result_path,
             min_spacing=70,
         )
 
@@ -105,8 +105,8 @@ def build(self):
 
         folder_group_l.addWidget(
             ui.combine_blocks(
-                second=self.btn_label_files,
-                first=self.lbl_label_files,
+                right_or_below=self.btn_label_files,
+                left_or_above=self.lbl_label_files,
                 min_spacing=70,
             ),
             alignment=ui.LEFT_AL,

napari_cellseg3d/plugin_dock.py

@@ -51,7 +51,9 @@ def __init__(self, parent: "napari.viewer.Viewer"):
         io_panel, io_layout = ui.make_container_widget(vertical=False)
         io_layout.addWidget(
             ui.combine_blocks(
-                first=self.button, second=self.time_label, horizontal=True
+                left_or_above=self.button,
+                right_or_below=self.time_label,
+                horizontal=True,
             )
         )  # , alignment=utils.ABS_AL)
         io_panel.setLayout(io_layout)

napari_cellseg3d/plugin_metrics.py

@@ -17,7 +17,7 @@
 
 
 class MetricsUtils(BasePluginFolder):
-    """Plugin to evaluate metrics between two sets of labels, ground truh and prediction"""
+    """Plugin to evaluate metrics between two sets of labels, ground truth and prediction"""
 
     def __init__(self, viewer: "napari.viewer.Viewer", parent):
         """Creates a MetricsUtils widget for computing and plotting dice metrics between labels.
@@ -47,7 +47,9 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent):
 
         self.btn_reset_plot = ui.make_button("Clear plots", self.remove_plots)
 
-        self.threshold_box = ui.make_n_spinboxes(min=0.1, max = 1, default=DEFAULT_THRESHOLD,step=0.1, double=True)
+        self.threshold_box = ui.make_n_spinboxes(
+            min=0.1, max=1, default=DEFAULT_THRESHOLD, step=0.1, double=True
+        )
 
         self.btn_result_path.setVisible(False)
         self.lbl_result_path.setVisible(False)
@@ -85,8 +87,8 @@ def build(self):
 
         metrics_group_l.addWidget(
             ui.combine_blocks(
-                second=self.btn_image_files,
-                first=self.lbl_image_files,
+                right_or_below=self.btn_image_files,
+                left_or_above=self.lbl_image_files,
                 min_spacing=70,
             ),
             alignment=ui.LEFT_AL,
@@ -96,8 +98,8 @@ def build(self):
 
         metrics_group_l.addWidget(
             ui.combine_blocks(
-                second=self.btn_label_files,
-                first=self.lbl_label_files,
+                right_or_below=self.btn_label_files,
+                left_or_above=self.lbl_label_files,
                 min_spacing=70,
             ),
             alignment=ui.LEFT_AL,
@@ -262,9 +264,11 @@ def compute_dice(self):
                     scores.append(utils.dice_coeff(p, ground))
                     scores.append(utils.dice_coeff(np.flip(p), ground))
                     for i in range(3):
-                        scores.append(utils.dice_coeff(np.flip(p, axis=i), ground))
+                        scores.append(
+                            utils.dice_coeff(np.flip(p, axis=i), ground)
+                        )
             else:
-                i=0
+                i = 0
                 scores.append(utils.dice_coeff(pred, ground))
             # if t < 1:
             #     for i in range(3):