555 add new logo to installer and update finetune gui (#556)

Updates to finetuning GUI + misc changes

Author: lufre1

.github/workflows/test.yaml

@@ -43,7 +43,7 @@ jobs:
         run: pip install --no-deps -e .
 
       - name: Run tests
-        uses: aganders3/headless-gui@v1
+        uses: aganders3/headless-gui@v2
         with:
           shell: bash -l {0}
           run: pytest

deployment/construct.yaml

@@ -3,9 +3,9 @@ version: 0.0.1
 license_file: ../LICENSE
 installer_type: pkg  #[osx]  # This will trigger pkg build on Mac Os. On windows and linux, native build will be done and this has no effect.
 environment: __MICROSAM_BUILD_ENV__
-welcome_image: ../doc/images/micro-sam-logo.png
-header_image: ../doc/images/micro-sam-logo.png
-icon_image: ../doc/images/micro-sam-logo.png
+welcome_image: ../doc/images/logo/logo.png
+header_image: ../doc/images/logo/logo.png
+icon_image: ../doc/images/logo/logo.png
 channels:
   - conda-forge
 welcome_text: Install Segment Anything for Microscopy.

micro_sam/bioimageio/model_export.py

@@ -85,7 +85,7 @@ def _create_test_inputs_and_outputs(
     point_prompt_path = os.path.join(tmp_dir, "point_prompts.npy")
     point_label_path = os.path.join(tmp_dir, "point_labels.npy")
     mask_prompt_path = os.path.join(tmp_dir, "mask_prompts.npy")
-    np.save(box_prompt_path, box_prompts)
+    np.save(box_prompt_path, box_prompts.astype("int64"))
     np.save(point_prompt_path, point_prompts)
     np.save(point_label_path, point_labels)
     np.save(mask_prompt_path, mask_prompts)
@@ -321,7 +321,6 @@ def export_sam_model(
                         id=spec.AxisId("object"),
                         size=spec.ARBITRARY_SIZE
                     ),
-                    # TODO double check the axis names
                     spec.ChannelAxis(channel_names=[spec.Identifier(bname) for bname in "hwxy"]),
                 ],
                 test_tensor=spec.FileDescr(source=input_paths["box_prompts"]),

micro_sam/sam_annotator/_tooltips.py

@@ -48,23 +48,22 @@
     "pattern": "Select a pattern for selecting files. E.g. '*.tif' to only select tif files. By default all files in the input folder are selected.",
     "is_volumetric": "Choose if the data you annotate is volumetric.",
   },
-  # TODO update these once we have finalized the names in the widget.
   "training": {
-    "checkpoint": "checkpoint",
-    "device": "device",
-    "initial_model": "initial_model",
-    "label_key": "label_key",
-    "label_path": "label_path",
-    "label_path_val": "label_path_val",
-    "name": "Name of the new model.",
-    "patch": "patch",
-    "raw_key": "raw_key",
-    "raw_path": "raw_path",
-    "raw_path_val": "raw_path_val",
-    "segmentation_decoder": "segmentation_decoder",
-    "output_path": "output_path",
-    "n_epochs": "n_epochs",
-    "configuration": "configuration",
+    "checkpoint": "Select a checkpoint (saved model) to resume training from.",
+    "device": "Select the computational device to use for processing.",
+    "initial_model": "Select the model name used as starting point for training.",
+    "label_key": "Define the key that holds to the segmentation labels. Use a pattern, e.g. \"*.tif\" select multiple files or an internal path for hdf5, zarr or similar formats.",
+    "label_path": "Specify the path to the segmentaiton labels for training. Can either point to a directory or single file.",
+    "label_path_val": "Specify the path to the segmentation labels for validation. Can either point to a directory or single file.",
+    "name": "Enter the name of the model that will be trained.",
+    "patch": "Select the size of image patches used for training.",
+    "raw_key": "Define the key that holds to the image data. Use a pattern, e.g. \"*.tif\" select multiple files or an internal path for hdf5, zarr or similar formats.",
+    "raw_path": "Specify the path to the image data for training. Can either point to a directory or single file.",
+    "raw_path_val": "Specify the path to the image data for training. Can either point to a directory or single file.",
+    "segmentation_decoder": "Choose whether to train with additional segmentation decoder or not.",
+    "output_path": "Specify the path where you want to save the trained model after the training process.",
+    "n_epochs": "Define the number of training epochs for your model.",
+    "configuration": "Specifiy the hardware configuration to use for training.",
   },
 }
 

micro_sam/sam_annotator/training_ui.py

@@ -52,42 +52,42 @@ def __init__(self, parent=None):
     def _create_options(self):
         self.raw_path = None
         _, layout = self._add_path_param(
-            "raw_path", self.raw_path, "both", placeholder="Image data ...",
+            "Path to images", self.raw_path, "both", placeholder="/path/to/images",
             tooltip=get_tooltip("training", "raw_path")
         )
         self.layout().addLayout(layout)
 
         self.raw_key = None
         _, layout = self._add_string_param(
-            "raw_key", self.raw_key, placeholder="Image data key ...",
+            "Image data key", self.raw_key, placeholder="e.g. \"*.tif\"",
             tooltip=get_tooltip("training", "raw_key")
         )
         self.layout().addLayout(layout)
 
         self.label_path = None
         _, layout = self._add_path_param(
-            "label_path", self.label_path, "both", placeholder="Label data ...",
+            "Path to labels", self.label_path, "both", placeholder="/path/to/labels",
             tooltip=get_tooltip("training", "label_path")
         )
         self.layout().addLayout(layout)
 
         self.label_key = None
         _, layout = self._add_string_param(
-            "label_key", self.label_key, placeholder="Label data key ...",
+            "Label data key", self.label_key, placeholder="e.g. \"*.tif\"",
             tooltip=get_tooltip("training", "label_key")
         )
         self.layout().addLayout(layout)
 
         self.configuration = _find_best_configuration()
         self.setting_dropdown, layout = self._add_choice_param(
-            "configuration", self.configuration, list(CONFIGURATIONS.keys()),
+            "Configuration", self.configuration, list(CONFIGURATIONS.keys()),
             tooltip=get_tooltip("training", "configuration")
         )
         self.layout().addLayout(layout)
 
         self.with_segmentation_decoder = True
         self.layout().addWidget(self._add_boolean_param(
-            "with_segmentation_decoder", self.with_segmentation_decoder,
+            "With segmentation decoder", self.with_segmentation_decoder,
             tooltip=get_tooltip("training", "segmentation_decoder")
         ))
 
@@ -100,28 +100,28 @@ def _create_settings(self):
         self.device = "auto"
         device_options = ["auto"] + util._available_devices()
         self.device_dropdown, layout = self._add_choice_param(
-            "device", self.device, device_options, tooltip=get_tooltip("training", "device")
+            "Device", self.device, device_options, tooltip=get_tooltip("training", "device")
         )
         setting_values.layout().addLayout(layout)
 
         self.patch_x, self.patch_y = 512, 512
         self.patch_x_param, self.patch_y_param, layout = self._add_shape_param(
-            ("patch_x", "patch_y"), (self.patch_x, self.patch_y), min_val=0, max_val=2048,
+            ("Patch size x", "Patch size y"), (self.patch_x, self.patch_y), min_val=0, max_val=2048,
             tooltip=get_tooltip("training", "patch")
         )
         setting_values.layout().addLayout(layout)
 
         # Paths for validation data.
         self.raw_path_val = None
         _, layout = self._add_path_param(
-            "raw_path_val", self.raw_path_val, "both", placeholder="Image data for validation ...",
+            "Path to validation images", self.raw_path_val, "both", placeholder="/path/to/images",
             tooltip=get_tooltip("training", "raw_path_val")
         )
         setting_values.layout().addLayout(layout)
 
         self.label_path_val = None
         _, layout = self._add_path_param(
-            "label_path_val", self.label_path_val, "both", placeholder="Label data for validation ...",
+            "Path to validation labels", self.label_path_val, "both", placeholder="/path/to/images",
             tooltip=get_tooltip("training", "label_path_val")
         )
         setting_values.layout().addLayout(layout)
@@ -130,31 +130,31 @@ def _create_settings(self):
         # on top of which the finetuning is run.
         self.name = "sam_model"
         self.name_param, layout = self._add_string_param(
-            "name", self.name, tooltip=get_tooltip("training", "name")
+            "Model name", self.name, tooltip=get_tooltip("training", "name")
         )
         setting_values.layout().addLayout(layout)
 
         self.initial_model = None
         self.initial_model_param, layout = self._add_string_param(
-            "initial_model", self.initial_model, tooltip=get_tooltip("training", "initial_model")
+            "Initial model", self.initial_model, tooltip=get_tooltip("training", "initial_model")
         )
         setting_values.layout().addLayout(layout)
 
         self.checkpoint = None
         self.checkpoint_param, layout = self._add_string_param(
-            "checkpoint", self.name, tooltip=get_tooltip("training", "checkpoint")
+            "Checkpoint", self.name, tooltip=get_tooltip("training", "checkpoint")
         )
         setting_values.layout().addLayout(layout)
 
         self.output_path = None
         self.output_path_param, layout = self._add_string_param(
-            "output_path", self.output_path, tooltip=get_tooltip("training", "output_path")
+            "Output Path", self.output_path, tooltip=get_tooltip("training", "output_path")
         )
         setting_values.layout().addLayout(layout)
 
         self.n_epochs = 100
         self.n_epochs_param, layout = self._add_int_param(
-            "n_epochs", self.n_epochs, tooltip=get_tooltip("training", "n_epochs"),
+            "Number of epochs", self.n_epochs, tooltip=get_tooltip("training", "n_epochs"),
             min_val=1, max_val=1000,
         )
         setting_values.layout().addLayout(layout)