SDK to 6.73.242. CheckpointInfo (#60)

dev_requirements.txt

@@ -1,4 +1,4 @@
-supervisely==6.73.229
+supervisely==6.73.242
 
 openmim
 ffmpeg-python==0.2.0

docker/Dockerfile

@@ -26,5 +26,5 @@ RUN mkdir -p /tmp/mmseg \
 && wget https://github.com/open-mmlab/mmsegmentation/archive/refs/tags/v0.23.0.tar.gz -P /tmp/mmseg \
 && tar -xvf /tmp/mmseg/v0.23.0.tar.gz -C /tmp/mmseg
 
-RUN pip3 install supervisely==6.73.229
-LABEL python_sdk_version=6.73.229
+RUN pip3 install supervisely==6.73.242
+LABEL python_sdk_version=6.73.242

serve/config.json

@@ -11,8 +11,8 @@
 		"serve"
 	],
 	"description": "Deploy model as REST API service",
-	"docker_image": "supervisely/mmseg:1.3.17",
-	"min_instance_version": "6.12.5",
+	"docker_image": "supervisely/mmseg:1.3.18",
+	"min_instance_version": "6.12.12",
 	"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==6.73.229
+supervisely==6.73.242
 
 openmim
 ffmpeg-python==0.2.0

serve/src/main.py

@@ -227,6 +227,26 @@ def load_model(
             self.model.eval()
             self.model = revert_sync_batchnorm(self.model)
 
+            # Set checkpoint info
+            if model_source == "Pretrained models":
+                custom_checkpoint_path = None
+            else:
+                sly.logger.info(f"Custom checkpoint path: {checkpoint_url}")
+                custom_checkpoint_path = checkpoint_url
+                file_id = self.api.file.get_info_by_path(team_id, checkpoint_url).id
+                checkpoint_url = self.api.file.get_url(file_id)
+            if arch_type is None:
+                arch_type = self.parse_arch_type(cfg)
+
+            self.checkpoint_info = sly.nn.inference.CheckpointInfo(
+                checkpoint_name=checkpoint_name,
+                model_name=self.selected_model_name,
+                architecture=arch_type,
+                checkpoint_url=checkpoint_url,
+                custom_checkpoint_path=custom_checkpoint_path,
+                model_source=model_source,
+            )
+
         except KeyError as e:
             raise KeyError(f"Error loading config file: {local_config_path}. Error: {e}")
 
@@ -354,6 +374,17 @@ def get_models(self):
                     model_config[model_meta["model_name"]]["checkpoints"].append(checkpoint_info)
         return model_config
 
+    def parse_arch_type(self, cfg: Config) -> str:
+        try:
+            arch_type = cfg.model.backbone.type
+            try:
+                arch_type += f"_{cfg.model.backbone.arch}"
+            except:
+                pass
+            return arch_type
+        except Exception as e:
+            sly.logger.warning(f"Error parsing model name: {e}")
+
     def get_classes(self) -> List[str]:
         return self.class_names  # e.g. ["cat", "dog", ...]
 

train/config.json

@@ -10,8 +10,8 @@
 		"train"
 	],
 	"description": "Dashboard to configure, start and monitor training",
-	"docker_image": "supervisely/mmseg:1.3.17",
-	"min_instance_version": "6.12.5",
+	"docker_image": "supervisely/mmseg:1.3.18",
+	"min_instance_version": "6.12.12",
 	"main_script": "train/src/main.py",
 	"gui_template": "train/src/gui.html",
 	"task_location": "workspace_tasks",

train/dev_requirements.txt

@@ -1,6 +1,6 @@
 # git+https://github.com/supervisely/supervisely.git@some-test-branch
 
-supervisely==6.73.229
+supervisely==6.73.242
 
 openmim
 ffmpeg-python==0.2.0