Add workflow and upgrade SDK to v6.73.178

serve/config.json

@@ -11,8 +11,8 @@
 		"serve"
 	],
 	"description": "Deploy model as REST API service",
-	"docker_image": "supervisely/mmseg:1.3.12",
-	"instance_version": "6.9.22",
+	"docker_image": "supervisely/mmseg:1.3.13",
+	"instance_version": "6.11.10",
 	"entrypoint": "python -m uvicorn main:m.app --app-dir ./serve/src --host 0.0.0.0 --port 8000 --ws websockets",
 	"port": 8000,
 	"task_location": "application_sessions",

serve/dev_requirements.txt

@@ -1,6 +1,6 @@
 # git+https://github.com/supervisely/supervisely.git@some-test-branch
 
-supervisely==6.73.119
+supervisely==6.73.178
 
 openmim
 ffmpeg-python==0.2.0

serve/src/main.py

@@ -33,6 +33,7 @@
     Widget,
 )
 from supervisely.io.fs import silent_remove
+import workflow as w
 
 root_source_path = str(Path(__file__).parents[2])
 app_source_path = str(Path(__file__).parents[1])
@@ -285,6 +286,8 @@ def load_on_device(
         ]
         self._model_meta = sly.ProjectMeta(obj_classes=sly.ObjClassCollection(obj_classes))
         print(f"✅ Model has been successfully loaded on {device.upper()} device")
+        if model_source == "Custom models":
+             w.workflow_input(self.api, custom_weights_link)
 
     def get_info(self) -> dict:
         info = super().get_info()

serve/src/workflow.py

@@ -0,0 +1,20 @@
+# Description: This file contains functionality for workflow feature
+
+import supervisely as sly
+
+
+def workflow_input(api: sly.Api, checkpoint_url: str):   
+    try:
+        node_settings = sly.WorkflowSettings(title="Serve Custom Model")
+        meta = sly.WorkflowMeta(node_settings=node_settings)
+        sly.logger.debug(f"Workflow Input: Checkpoint URL - {checkpoint_url}")
+        if checkpoint_url and api.file.exists(sly.env.team_id(), checkpoint_url):
+            api.app.workflow.add_input_file(checkpoint_url, model_weight=True, meta=meta)
+        else:
+            sly.logger.debug(f"Checkpoint {checkpoint_url} not found in Team Files. Cannot set workflow input")
+    except Exception as e:
+        sly.logger.debug(f"Failed to add input to the workflow: {repr(e)}")
+
+
+def workflow_output(api: sly.Api):
+    raise NotImplementedError("Method is not implemented yet")

train/config.json

@@ -1,33 +1,33 @@
 {
-	"name": "Train MMSegmentation",
-	"type": "app",
-	"categories": [
-		"neural network",
-		"images",
-		"videos",
-		"semantic segmentation",
-		"segmentation & tracking",
-		"train"
-	],
-	"description": "Dashboard to configure, start and monitor training",
-	"docker_image": "supervisely/mmseg:1.3.12",
-	"min_instance_version": "6.9.22",
-	"main_script": "train/src/main.py",
-	"gui_template": "train/src/gui.html",
-	"task_location": "workspace_tasks",
-	"need_gpu": true,
-	"gpu": "required",
-	"isolate": true,
-	"icon": "https://i.imgur.com/GaEFmkH.png",
-	"icon_cover": true,
-	"context_menu": {
-		"target": ["images_project"],
-		"context_root": "Neural Networks",
-		"context_category": "MM Segmentation"
-	},
-	"poster": "https://user-images.githubusercontent.com/48245050/182847473-9a35f213-c27b-4abd-bd64-c73bf80fb056.jpg",
-	"community_agent": false,
-	"license": {
-		"type": "Apache-2.0"
-	}
+  "name": "Train MMSegmentation",
+  "type": "app",
+  "categories": [
+    "neural network",
+    "images",
+    "videos",
+    "semantic segmentation",
+    "segmentation & tracking",
+    "train"
+  ],
+  "description": "Dashboard to configure, start and monitor training",
+  "docker_image": "supervisely/mmseg:1.3.13",
+  "min_instance_version": "6.11.10",
+  "main_script": "train/src/main.py",
+  "gui_template": "train/src/gui.html",
+  "task_location": "workspace_tasks",
+  "need_gpu": true,
+  "gpu": "required",
+  "isolate": true,
+  "icon": "https://i.imgur.com/GaEFmkH.png",
+  "icon_cover": true,
+  "context_menu": {
+    "target": ["images_project"],
+    "context_root": "Neural Networks",
+    "context_category": "MM Segmentation"
+  },
+  "poster": "https://user-images.githubusercontent.com/48245050/182847473-9a35f213-c27b-4abd-bd64-c73bf80fb056.jpg",
+  "community_agent": false,
+  "license": {
+    "type": "Apache-2.0"
+  }
 }

train/dev_requirements.txt

@@ -1,6 +1,6 @@
 # git+https://github.com/supervisely/supervisely.git@some-test-branch
 
-supervisely==6.73.119
+supervisely==6.73.178
 
 openmim
 ffmpeg-python==0.2.0

train/src/sly_globals.py

@@ -62,3 +62,5 @@
     shutil.copytree(f"/tmp/mmseg/mmsegmentation-{mmseg_ver}/configs", configs_dir)
     models_cnt = len(os.listdir(configs_dir)) - 1
     sly.logger.info(f"Found {models_cnt} folders in {configs_dir} directory.")
+
+sly_mmseg_generated_metadata = None # for project Workflow purposes

train/src/ui/monitoring.py

@@ -12,6 +12,7 @@
 from mmseg.datasets import build_dataset
 from mmseg.models import build_segmentor
 from init_cfg import init_cfg
+import workflow as w
 
 # ! required to be left here despite not being used
 import sly_imgaugs
@@ -216,7 +217,7 @@ def upload_monitor(monitor, api: sly.Api, task_id, progress: sly.Progress):
     )
 
     # generate metadata file
-    g.sly_mmseg.generate_metadata(
+    g.sly_mmseg_generated_metadata = g.sly_mmseg.generate_metadata(
         app_name=g.sly_mmseg.app_name,
         task_id=g.task_id,
         artifacts_folder=remote_artifacts_dir,
@@ -347,7 +348,10 @@ def train(api: sly.Api, task_id, context, state, app_logger):
         ]
         g.api.app.set_fields(g.task_id, fields)
 
-        # stop application
+        w.workflow_input(api, g.project_info, state)
+        w.workflow_output(api, g.sly_mmseg_generated_metadata, state)
+
+        # stop application        
         g.my_app.stop()
     except Exception as e:
         g.api.app.set_field(task_id, "state.started", False)

train/src/workflow.py

@@ -0,0 +1,81 @@
+# Description: This file contains versioning and workflow features
+
+import supervisely as sly
+
+from supervisely.api.file_api import FileInfo
+
+def workflow_input(api: sly.Api, project_info: sly.ProjectInfo, state: dict = None):   
+    try:       
+        if project_info.type != sly.ProjectType.IMAGES.__str__():
+            sly.logger.info(f"{project_info.type =} is not '{sly.ProjectType.IMAGES.__str__()}'. Project version will not be created.")
+            project_version_id = None
+        else:
+            project_version_id = api.project.version.create(
+                project_info, "Train MMSegmentation", f"This backup was created automatically by Supervisely before the Train MMSegmentation task with ID: {api.task_id}"
+            )
+    except Exception as e:
+        sly.logger.warning(f"Failed to create a project version: {repr(e)}")
+        project_version_id = None
+
+    try:
+        file_info = None
+        if project_version_id is None:
+            project_version_id = project_info.version.get("id", None) if project_info.version else None
+        api.app.workflow.add_input_project(project_info.id, version_id=project_version_id)
+        if state.get("weightsInitialization", None) == "custom":
+            file_path = state.get("weightsPath", None)
+            if file_path is None:
+                sly.logger.debug("Workflow Input: weights file path is not specified. Cannot add input file to the workflow.")
+            file_info = api.file.get_info_by_path(sly.env.team_id(), file_path)
+        if file_info is not None:
+            api.app.workflow.add_input_file(file_info, model_weight=True)
+        sly.logger.debug(f"Workflow Input: Project ID - {project_info.id}, Project Version ID - {project_version_id}, Input File - {True if file_info else False}")
+    except Exception as e:
+        sly.logger.debug(f"Failed to add input to the workflow: {repr(e)}")
+
+
+def workflow_output(api: sly.Api, mmseg_generated_metadata: dict, state:dict):
+    try:
+        checkpoints_list = mmseg_generated_metadata.get("checkpoints", [])
+        if len(checkpoints_list) == 0:
+            sly.logger.debug("Workflow Output: No checkpoints found. Cannot set workflow output.")
+            best_filename_info = None
+        else:
+            best_checkpoints = []
+            new_infos = [FileInfo(*checkpoint) for checkpoint in checkpoints_list]
+            for info in new_infos:
+                best_checkpoints.append(info) if "best" in info.name else None
+            if len(best_checkpoints) > 1:
+                best_filename_info = sorted(best_checkpoints, key=lambda x: x.name, reverse=True)[0]
+            else:
+                best_filename_info = best_checkpoints[0]
+
+
+        module_id = api.task.get_info_by_id(api.task_id).get("meta", {}).get("app", {}).get("id")
+
+        if state.get("weightsInitialization", None) == "custom":
+            node_custom_title = "Train Custom Model"
+        else:
+            node_custom_title = None
+        if best_filename_info:
+            node_settings = sly.WorkflowSettings(
+                    title=node_custom_title, 
+                    url = f"/apps/{module_id}/sessions/{api.task_id}" if module_id else f"apps/sessions/{api.task_id}",
+                    url_title= "Show Results",
+                )
+            relation_settings = sly.WorkflowSettings(
+                    title="Checkpoints",
+                    icon="folder",
+                    icon_color = "#FFA500",
+                    icon_bg_color ="#FFE8BE",
+                    url=f"/files/{best_filename_info.id}/true", 
+                    url_title = "Open Folder",
+                )
+            meta = sly.WorkflowMeta(relation_settings, node_settings)
+            api.app.workflow.add_output_file(best_filename_info, model_weight=True, meta=meta)
+            sly.logger.debug(f"Workflow Output: Node custom title - {node_custom_title}, Best filename - {best_filename_info}")
+            sly.logger.debug(f"Workflow Output: Meta \n    {meta.as_dict}")
+        else:
+            sly.logger.debug(f"File {best_filename_info} not found in Team Files. Cannot set workflow output.")
+    except Exception as e:
+        sly.logger.debug(f"Failed to add output to the workflow: {repr(e)}")