Fix e2e failed case of visual prompting task

src/otx/algorithms/visual_prompting/tasks/inference.py

@@ -31,6 +31,7 @@
 from pytorch_lightning import LightningModule, Trainer
 from pytorch_lightning.callbacks import TQDMProgressBar
 
+from otx.algorithms.common.utils import set_random_seed
 from otx.algorithms.common.utils.logger import get_logger
 from otx.algorithms.visual_prompting.adapters.pytorch_lightning.callbacks import (
     InferenceCallback,
@@ -104,12 +105,22 @@ def __init__(self, task_environment: TaskEnvironment, output_path: Optional[str]
         self.precision = [ModelPrecision.FP32]
         self.optimization_type = ModelOptimizationType.MO
 
-        self.model = self.load_model(otx_model=task_environment.model)
-
         self.trainer: Trainer
 
         self.timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
 
+    def set_seed(self):
+        """Set seed and deterministic."""
+        if self.seed is None:
+            # If the seed is not present via task.train, it will be found in the recipe.
+            self.seed = self.config.get("seed", 5)
+        if not self.deterministic:
+            # deterministic is the same.
+            self.deterministic = self.config.get("deterministic", False)
+        self.config["seed"] = self.seed
+        self.config["deterministic"] = self.deterministic
+        set_random_seed(self.seed, logger, self.deterministic)
+
     def get_config(self) -> Union[DictConfig, ListConfig]:
         """Get Visual Prompting Config from task environment.
 
@@ -226,6 +237,7 @@ def infer(self, dataset: DatasetEntity, inference_parameters: InferenceParameter
             DatasetEntity: Output dataset with predictions.
         """
         logger.info("Performing inference on the validation set using the base torch model.")
+        self.model = self.load_model(otx_model=self.task_environment.model)
         datamodule = OTXVisualPromptingDataModule(config=self.config.dataset, dataset=dataset)
 
         logger.info("Inference Configs '%s'", self.config)
@@ -330,6 +342,7 @@ def export(  # noqa: D102
                 "The saliency maps and representation vector outputs will not be dumped in the exported model."
             )
 
+        self.model = self.load_model(otx_model=self.task_environment.model)
         if export_type == ExportType.ONNX:
             output_model.model_format = ModelFormat.ONNX
             output_model.optimization_type = ModelOptimizationType.ONNX

src/otx/algorithms/visual_prompting/tasks/train.py

@@ -16,7 +16,7 @@
 
 from typing import Optional
 
-from pytorch_lightning import Trainer, seed_everything
+from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import (
     EarlyStopping,
     LearningRateMonitor,
@@ -62,13 +62,15 @@ def train(  # noqa: D102
 
         logger.info("Training the model.")
 
-        if seed:
-            logger.info(f"Setting seed to {seed}")
-            seed_everything(seed, workers=True)
+        self.seed = seed
+        self.deterministic = deterministic
+        self.set_seed()
         self.config.trainer.deterministic = "warn" if deterministic else deterministic
 
         logger.info("Training Configs '%s'", self.config)
 
+        self.model = self.load_model(otx_model=self.task_environment.model)
+
         datamodule = OTXVisualPromptingDataModule(config=self.config.dataset, dataset=dataset)
         loggers = CSVLogger(save_dir=self.output_path, name=".", version=self.timestamp)
         callbacks = [

tests/unit/algorithms/visual_prompting/tasks/test_inference.py

@@ -94,7 +94,8 @@ def test_load_model_without_otx_model_or_with_lightning_ckpt(
             "otx.algorithms.visual_prompting.adapters.pytorch_lightning.models.SegmentAnything"
         )
 
-        load_inference_task(path=path, resume=resume)
+        inference_task = load_inference_task(path=path, resume=resume)
+        inference_task.load_model(otx_model=inference_task.task_environment.model)
 
         mocker_segment_anything.assert_called_once()
 
@@ -111,7 +112,8 @@ def test_load_model_with_pytorch_pth(self, mocker, load_inference_task, resume:
             return_value=dict(config=dict(model=dict(backbone="sam_vit_b")), model={}),
         )
 
-        load_inference_task(path="checkpoint.pth", resume=resume)
+        inference_task = load_inference_task(path="checkpoint.pth", resume=resume)
+        inference_task.load_model(otx_model=inference_task.task_environment.model)
 
         mocker_segment_anything.assert_called_once()
         mocker_io_bytes_io.assert_called_once()
@@ -162,6 +164,7 @@ def test_model_info(self, mocker, load_inference_task):
         )
 
         inference_task = load_inference_task(output_path=None)
+        inference_task.model = inference_task.load_model(otx_model=inference_task.task_environment.model)
         setattr(inference_task, "trainer", None)
         mocker.patch.object(inference_task, "trainer")