Merge branch 'develop' into andrew/more-cases-for-copying-instance

docs/guides/cli.md

@@ -36,8 +36,8 @@ optional arguments:
 
 ```none
 usage: sleap-train [-h] [--video-paths VIDEO_PATHS] [--val_labels VAL_LABELS]
-                   [--test_labels TEST_LABELS] [--tensorboard] [--save_viz]
-                   [--zmq] [--run_name RUN_NAME] [--prefix PREFIX]
+                   [--test_labels TEST_LABELS] [--tensorboard] [--save_viz] 
+                   [--keep_viz] [--zmq] [--run_name RUN_NAME] [--prefix PREFIX]
                    [--suffix SUFFIX]
                    training_job_path [labels_path]
 
@@ -68,6 +68,8 @@ optional arguments:
   --save_viz            Enable saving of prediction visualizations to the run
                         folder if not already specified in the training job
                         config.
+  --keep_viz            Keep prediction visualization images in the run
+                        folder after training if --save_viz is enabled.
   --zmq                 Enable ZMQ logging (for GUI) if not already specified
                         in the training job config.
   --run_name RUN_NAME   Run name to use when saving file, overrides other run

docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb

@@ -335,7 +335,7 @@
       "        \"runs_folder\": \"models\",\n",
       "        \"tags\": [],\n",
       "        \"save_visualizations\": true,\n",
-      "        \"delete_viz_images\": true,\n",
+      "        \"keep_viz_images\": true,\n",
       "        \"zip_outputs\": false,\n",
       "        \"log_to_csv\": true,\n",
       "        \"checkpointing\": {\n",
@@ -727,7 +727,7 @@
       "        \"runs_folder\": \"models\",\n",
       "        \"tags\": [],\n",
       "        \"save_visualizations\": true,\n",
-      "        \"delete_viz_images\": true,\n",
+      "        \"keep_viz_images\": true,\n",
       "        \"zip_outputs\": false,\n",
       "        \"log_to_csv\": true,\n",
       "        \"checkpointing\": {\n",

sleap/config/pipeline_form.yaml

@@ -286,6 +286,11 @@ training:
   type: bool
   default: true
 
+- name: _keep_viz
+  label: Keep Prediction Visualization Images After Training
+  type: bool
+  default: false
+
 - name: _predict_frames
   label: Predict On
   type: list

sleap/gui/app.py

@@ -53,6 +53,8 @@
 from logging import getLogger
 from pathlib import Path
 from typing import Callable, List, Optional, Tuple
+import sys
+import subprocess
 
 from qtpy import QtCore, QtGui
 from qtpy.QtCore import QEvent, Qt
@@ -84,7 +86,7 @@
 from sleap.io.video import available_video_exts
 from sleap.prefs import prefs
 from sleap.skeleton import Skeleton
-from sleap.util import parse_uri_path
+from sleap.util import parse_uri_path, get_config_file
 
 
 logger = getLogger(__name__)
@@ -515,6 +517,13 @@ def add_submenu_choices(menu, title, options, key):
             fileMenu, "reset prefs", "Reset preferences to defaults...", self.resetPrefs
         )
 
+        add_menu_item(
+            fileMenu,
+            "open preference directory",
+            "Open Preferences Directory...",
+            self.openPrefs,
+        )
+
         fileMenu.addSeparator()
         add_menu_item(fileMenu, "close", "Quit", self.close)
 
@@ -1330,6 +1339,20 @@ def resetPrefs(self):
         )
         msg.exec_()
 
+    def openPrefs(self):
+        """Open preference file directory"""
+        pref_path = get_config_file("preferences.yaml")
+        # Make sure the pref_path is a directory rather than a file
+        if pref_path.is_file():
+            pref_path = pref_path.parent
+        # Open the file explorer at the folder containing the preferences.yaml file
+        if sys.platform == "win32":
+            subprocess.Popen(["explorer", str(pref_path)])
+        elif sys.platform == "darwin":
+            subprocess.Popen(["open", str(pref_path)])
+        else:
+            subprocess.Popen(["xdg-open", str(pref_path)])
+
     def _update_track_menu(self):
         """Updates track menu options."""
         self.track_menu.clear()

sleap/gui/learning/runners.py

@@ -1,4 +1,5 @@
 """Run training/inference in background process via CLI."""
+
 import abc
 import attr
 import os
@@ -500,9 +501,11 @@ def write_pipeline_files(
                 "data_path": os.path.basename(data_path),
                 "models": [Path(p).as_posix() for p in new_cfg_filenames],
                 "output_path": prediction_output_path,
-                "type": "labels"
-                if type(item_for_inference) == DatasetItemForInference
-                else "video",
+                "type": (
+                    "labels"
+                    if type(item_for_inference) == DatasetItemForInference
+                    else "video"
+                ),
                 "only_suggested_frames": only_suggested_frames,
                 "tracking": tracking_args,
             }
@@ -544,6 +547,7 @@ def run_learning_pipeline(
     """
 
     save_viz = inference_params.get("_save_viz", False)
+    keep_viz = inference_params.get("_keep_viz", False)
 
     if "movenet" in inference_params["_pipeline"]:
         trained_job_paths = [inference_params["_pipeline"]]
@@ -557,6 +561,7 @@ def run_learning_pipeline(
             inference_params=inference_params,
             gui=True,
             save_viz=save_viz,
+            keep_viz=keep_viz,
         )
 
         # Check that all the models were trained
@@ -585,6 +590,7 @@ def run_gui_training(
     inference_params: Dict[str, Any],
     gui: bool = True,
     save_viz: bool = False,
+    keep_viz: bool = False,
 ) -> Dict[Text, Text]:
     """
     Runs training for each training job.
@@ -594,6 +600,7 @@ def run_gui_training(
         config_info_list: List of ConfigFileInfo with configs for training.
         gui: Whether to show gui windows and process gui events.
         save_viz: Whether to save visualizations from training.
+        keep_viz: Whether to keep prediction visualization images after training.
 
     Returns:
         Dictionary, keys are head name, values are path to trained config.
@@ -683,6 +690,7 @@ def waiting():
                 video_paths=video_path_list,
                 waiting_callback=waiting,
                 save_viz=save_viz,
+                keep_viz=keep_viz,
             )
 
             if ret == "success":
@@ -825,6 +833,7 @@ def train_subprocess(
     video_paths: Optional[List[Text]] = None,
     waiting_callback: Optional[Callable] = None,
     save_viz: bool = False,
+    keep_viz: bool = False,
 ):
     """Runs training inside subprocess."""
     run_path = job_config.outputs.run_path
@@ -853,6 +862,8 @@ def train_subprocess(
 
         if save_viz:
             cli_args.append("--save_viz")
+        if keep_viz:
+            cli_args.append("--keep_viz")
 
         # Use cli arg since cli ignores setting in config
         if job_config.outputs.tensorboard.write_logs:

sleap/gui/overlays/tracks.py

@@ -1,17 +1,16 @@
 """Track trail and track list overlays."""
 
+from typing import Dict, Iterable, List, Optional, Tuple
+
+import attr
+from qtpy import QtCore, QtGui
+
 from sleap.gui.overlays.base import BaseOverlay
+from sleap.gui.widgets.video import QtTextWithBackground
 from sleap.instance import Track
 from sleap.io.dataset import Labels
 from sleap.io.video import Video
 from sleap.prefs import prefs
-from sleap.gui.widgets.video import QtTextWithBackground
-
-import attr
-
-from typing import Iterable, List, Optional, Dict
-
-from qtpy import QtCore, QtGui
 
 
 @attr.s(auto_attribs=True)
@@ -58,7 +57,9 @@ def get_shade_options(cls):
 
         return {"Dark": 0.6, "Normal": 1.0, "Light": 1.25}
 
-    def get_track_trails(self, frame_selection: Iterable["LabeledFrame"]):
+    def get_track_trails(
+        self, frame_selection: Iterable["LabeledFrame"]
+    ) -> Optional[Dict[Track, List[List[Tuple[float, float]]]]]:
         """Get data needed to draw track trail.
 
         Args:
@@ -154,6 +155,8 @@ def add_to_scene(self, video: Video, frame_idx: int):
         frame_selection = self.get_frame_selection(video, frame_idx)
 
         all_track_trails = self.get_track_trails(frame_selection)
+        if all_track_trails is None:
+            return
 
         for track, trails in all_track_trails.items():
             trail_color = tuple(

sleap/nn/config/outputs.py

@@ -151,8 +151,8 @@ class OutputsConfig:
         save_visualizations: If True, will render and save visualizations of the model
             predictions as PNGs to "{run_folder}/viz/{split}.{epoch:04d}.png", where the
             split is one of "train", "validation", "test".
-        delete_viz_images: If True, delete the saved visualizations after training
-            completes. This is useful to reduce the model folder size if you do not need
+        keep_viz_images: If True, keep the saved visualization images after training
+            completes. This is useful unchecked to reduce the model folder size if you do not need
             to keep the visualization images.
         zip_outputs: If True, compress the run folder to a zip file. This will be named
             "{run_folder}.zip".
@@ -170,7 +170,7 @@ class OutputsConfig:
     runs_folder: Text = "models"
     tags: List[Text] = attr.ib(factory=list)
     save_visualizations: bool = True
-    delete_viz_images: bool = True
+    keep_viz_images: bool = False
     zip_outputs: bool = False
     log_to_csv: bool = True
     checkpointing: CheckpointingConfig = attr.ib(factory=CheckpointingConfig)

sleap/nn/training.py

@@ -946,7 +946,7 @@ def train(self):
         if self.config.outputs.save_outputs:
             if (
                 self.config.outputs.save_visualizations
-                and self.config.outputs.delete_viz_images
+                and not self.config.outputs.keep_viz_images
             ):
                 self.cleanup()
 
@@ -997,7 +997,7 @@ def cleanup(self):
 
     def package(self):
         """Package model folder into a zip file for portability."""
-        if self.config.outputs.delete_viz_images:
+        if not self.config.outputs.keep_viz_images:
             self.cleanup()
         logger.info(f"Packaging results to: {self.run_path}.zip")
         shutil.make_archive(
@@ -1864,6 +1864,14 @@ def create_trainer_using_cli(args: Optional[List] = None):
             "already specified in the training job config."
         ),
     )
+    parser.add_argument(
+        "--keep_viz",
+        action="store_true",
+        help=(
+            "Keep prediction visualization images in the run folder after training when "
+            "--save_viz is enabled."
+        ),
+    )
     parser.add_argument(
         "--zmq",
         action="store_true",
@@ -1949,6 +1957,7 @@ def create_trainer_using_cli(args: Optional[List] = None):
     if args.suffix != "":
         job_config.outputs.run_name_suffix = args.suffix
     job_config.outputs.save_visualizations |= args.save_viz
+    job_config.outputs.keep_viz_images = args.keep_viz
     if args.labels_path == "":
         args.labels_path = None
     args.video_paths = args.video_paths.split(",")

sleap/training_profiles/baseline.centroid.json

@@ -116,6 +116,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/baseline_large_rf.bottomup.json

@@ -125,6 +125,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/baseline_large_rf.single.json

@@ -116,6 +116,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/baseline_large_rf.topdown.json

@@ -117,6 +117,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/baseline_medium_rf.bottomup.json

@@ -125,6 +125,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/baseline_medium_rf.single.json

@@ -116,6 +116,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/baseline_medium_rf.topdown.json

@@ -117,6 +117,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/pretrained.bottomup.json

@@ -122,6 +122,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/pretrained.centroid.json

@@ -113,6 +113,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/pretrained.single.json

@@ -113,6 +113,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,

sleap/training_profiles/pretrained.topdown.json

@@ -114,6 +114,7 @@
         "runs_folder": "models",
         "tags": [],
         "save_visualizations": true,
+        "keep_viz_images": false,
         "log_to_csv": true,
         "checkpointing": {
             "initial_model": false,