Refactor operator imports and enhance pipeline generator functionality

- Removed deprecated operators from the MONAI Deploy SDK and updated the import paths in the application template to reflect the new structure.
- Introduced new operators such as GenericDirectoryScanner and ImageFileLoader for improved file handling.
- Enhanced the NiftiDataLoader to handle various dimensionalities correctly and added logging for unexpected shapes.
- Updated the pipeline generator to include new operators and refined the requirements for dependencies in the configuration files.
- Added comprehensive tests for the new operators and updated existing tests to ensure functionality and correctness.

Signed-off-by: Victor Chang <[email protected]>

Author: mocsharp

monai/deploy/operators/__init__.py

@@ -21,27 +21,18 @@
     DICOMSeriesToVolumeOperator
     DICOMTextSRWriterOperator
     EquipmentInfo
-    GenericDirectoryScanner
-    ImageFileLoader
-    ImageOverlayWriter
     InferenceOperator
     InfererType
     IOMapping
-    JSONResultsWriter
-    Llama3VILAInferenceOperator
     ModelInfo
     MonaiBundleInferenceOperator
-    MonaiClassificationOperator
     MonaiSegInferenceOperator
     NiftiDataLoader
-    NiftiWriter
     PNGConverterOperator
-    PromptsLoaderOperator
     PublisherOperator
     SegmentDescription
     STLConversionOperator
     STLConverter
-    VLMResultsWriterOperator
 """
 
 # If needed, can choose to expose some or all of Holoscan SDK built-in operators.
@@ -67,24 +58,21 @@
     EquipmentInfo,
     ModelInfo,
 )
-from .generic_directory_scanner_operator import GenericDirectoryScanner
-from .image_file_loader_operator import ImageFileLoader
-from .image_overlay_writer_operator import ImageOverlayWriter
+
 from .inference_operator import InferenceOperator
-from .json_results_writer_operator import JSONResultsWriter
-from .llama3_vila_inference_operator import Llama3VILAInferenceOperator
+
 from .monai_bundle_inference_operator import (
     BundleConfigNames,
     IOMapping,
     MonaiBundleInferenceOperator,
 )
-from .monai_classification_operator import MonaiClassificationOperator
+
 from .monai_seg_inference_operator import InfererType, MonaiSegInferenceOperator
 
-from .nifti_writer_operator import NiftiWriter
+
 from .nii_data_loader_operator import NiftiDataLoader
 from .png_converter_operator import PNGConverterOperator
-from .prompts_loader_operator import PromptsLoaderOperator
+
 from .publisher_operator import PublisherOperator
 from .stl_conversion_operator import STLConversionOperator, STLConverter
-from .vlm_results_writer_operator import VLMResultsWriterOperator
+

monai/deploy/operators/nii_data_loader_operator.py

@@ -80,7 +80,22 @@ def convert_and_save(self, nii_path):
         image_reader = SimpleITK.ImageFileReader()
         image_reader.SetFileName(str(nii_path))
         image = image_reader.Execute()
-        image_np = np.transpose(SimpleITK.GetArrayFromImage(image), [2, 1, 0])
+        image_np = SimpleITK.GetArrayFromImage(image)
+        
+        # Handle different dimensionalities properly
+        if image_np.ndim == 3:
+            # Standard 3D volume: transpose from (z, y, x) to (x, y, z)
+            image_np = np.transpose(image_np, [2, 1, 0])
+        elif image_np.ndim == 4:
+            # 4D volume with channels: (c, z, y, x) to (c, x, y, z)
+            image_np = np.transpose(image_np, [0, 3, 2, 1])
+        elif image_np.ndim == 2:
+            # 2D slice: transpose from (y, x) to (x, y)
+            image_np = np.transpose(image_np, [1, 0])
+        else:
+            # For other dimensions, log a warning and return as-is
+            self._logger.warning(f"Unexpected {image_np.ndim}D NIfTI file shape {image_np.shape} from {nii_path}, returning without transpose")
+        
         return image_np
 
 

tools/pipeline-generator/pipeline_generator/config/config.yaml

@@ -43,12 +43,27 @@ endpoints:
       - model_id: "MONAI/Llama3-VILA-M3-3B"
         input_type: "custom"
         output_type: "custom"
+        dependencies:
+        - transformers>=4.44.0
+        - torch>=2.0.0
+        - Pillow>=8.0.0
+        - PyYAML>=6.0
       - model_id: "MONAI/Llama3-VILA-M3-8B"
         input_type: "custom"
         output_type: "custom"
+        dependencies:
+        - transformers>=4.44.0
+        - torch>=2.0.0
+        - Pillow>=8.0.0
+        - PyYAML>=6.0
       - model_id: "MONAI/Llama3-VILA-M3-13B"
         input_type: "custom"
         output_type: "custom"
+        dependencies:
+        - transformers>=4.44.0
+        - torch>=2.0.0
+        - Pillow>=8.0.0
+        - PyYAML>=6.0
 
 additional_models:
   - model_id: "LGAI-EXAONE/EXAONEPath"

tools/pipeline-generator/pipeline_generator/generator/app_generator.py

@@ -446,14 +446,74 @@ def _copy_additional_files(self, output_dir: Path, context: Dict[str, Any]) -> N
             output_dir: Output directory
             context: Template context
         """
-        # No need for custom operators anymore - using SDK operators
+        # Copy needed operators to generated application
+        self._copy_operators(output_dir, context)
 
         # Generate requirements.txt
         self._generate_requirements(output_dir, context)
 
         # Generate README.md
         self._generate_readme(output_dir, context)
 
+    def _copy_operators(self, output_dir: Path, context: Dict[str, Any]) -> None:
+        """Copy needed operators to the generated application.
+
+        Args:
+            output_dir: Output directory
+            context: Template context
+        """
+        import shutil
+        
+        # Map operator usage based on context
+        needed_operators = []
+        
+        input_type = context.get('input_type', '')
+        output_type = context.get('output_type', '')
+        task = context.get('task', '').lower()
+        
+        # Determine which operators are needed based on the application type
+        if input_type == "image":
+            needed_operators.extend([
+                'generic_directory_scanner_operator.py',
+                'image_file_loader_operator.py'
+            ])
+        elif input_type == "custom":
+            needed_operators.extend([
+                'llama3_vila_inference_operator.py',
+                'prompts_loader_operator.py',
+                'vlm_results_writer_operator.py'
+            ])
+        elif input_type == "nifti":
+            needed_operators.append('generic_directory_scanner_operator.py')
+            
+        if output_type == "json":
+            needed_operators.append('json_results_writer_operator.py')
+        elif output_type == "image_overlay":
+            needed_operators.append('image_overlay_writer_operator.py')
+        elif output_type == "nifti":
+            needed_operators.append('nifti_writer_operator.py')
+            
+        if "classification" in task and input_type == "image":
+            needed_operators.append('monai_classification_operator.py')
+            
+        # Remove duplicates
+        needed_operators = list(set(needed_operators))
+        
+        if needed_operators:
+            # Get the operators directory in templates
+            operators_dir = Path(__file__).parent.parent / "templates" / "operators"
+            
+            logger.info(f"Copying {len(needed_operators)} operators to generated application")
+            
+            for operator_file in needed_operators:
+                src_path = operators_dir / operator_file
+                if src_path.exists():
+                    dst_path = output_dir / operator_file
+                    shutil.copy2(src_path, dst_path)
+                    logger.debug(f"Copied operator: {operator_file}")
+                else:
+                    logger.warning(f"Operator file not found: {src_path}")
+
     def _generate_requirements(self, output_dir: Path, context: Dict[str, Any]) -> None:
         """Generate requirements.txt file.
 

tools/pipeline-generator/pipeline_generator/templates/app.py.j2

@@ -43,33 +43,33 @@ from monai.deploy.operators.stl_conversion_operator import STLConversionOperator
 
 {% endif %}
 {% elif input_type == "image" %}
-from monai.deploy.operators.generic_directory_scanner_operator import GenericDirectoryScanner
-from monai.deploy.operators.image_file_loader_operator import ImageFileLoader
+from generic_directory_scanner_operator import GenericDirectoryScanner
+from image_file_loader_operator import ImageFileLoader
 
 {% elif input_type == "custom" %}
-from monai.deploy.operators.llama3_vila_inference_operator import Llama3VILAInferenceOperator
+from llama3_vila_inference_operator import Llama3VILAInferenceOperator
 
 # Custom operators for vision-language models
-from monai.deploy.operators.prompts_loader_operator import PromptsLoaderOperator
-from monai.deploy.operators.vlm_results_writer_operator import VLMResultsWriterOperator
+from prompts_loader_operator import PromptsLoaderOperator
+from vlm_results_writer_operator import VLMResultsWriterOperator
 
 {% else %}
-from monai.deploy.operators.generic_directory_scanner_operator import GenericDirectoryScanner
+from generic_directory_scanner_operator import GenericDirectoryScanner
 from monai.deploy.operators.nii_data_loader_operator import NiftiDataLoader
 
 {% endif %}
 {% if output_type == "json" %}
-from monai.deploy.operators.json_results_writer_operator import JSONResultsWriter
+from json_results_writer_operator import JSONResultsWriter
 
 {% elif output_type == "image_overlay" %}
-from monai.deploy.operators.image_overlay_writer_operator import ImageOverlayWriter
+from image_overlay_writer_operator import ImageOverlayWriter
 
-{% elif not use_dicom %}
-from monai.deploy.operators.nifti_writer_operator import NiftiWriter
+{% elif not use_dicom and input_type != "custom" %}
+from nifti_writer_operator import NiftiWriter
 
 {% endif %}
 {% if "classification" in task.lower() and input_type == "image" %}
-from monai.deploy.operators.monai_classification_operator import MonaiClassificationOperator
+from monai_classification_operator import MonaiClassificationOperator
 
 {% elif not (input_type == "custom" and output_type == "custom") %}
 from monai.deploy.operators.monai_bundle_inference_operator import (

…rs/generic_directory_scanner_operator.py …rs/generic_directory_scanner_operator.pymonai/deploy/operators/generic_directory_scanner_operator.py renamed to tools/pipeline-generator/pipeline_generator/templates/operators/generic_directory_scanner_operator.py monai/deploy/operators/generic_directory_scanner_operator.py renamed to tools/pipeline-generator/pipeline_generator/templates/operators/generic_directory_scanner_operator.py

@@ -25,8 +25,8 @@
 AutoTokenizer, _ = optional_import("transformers", name="AutoTokenizer")
 
 PILImage, _ = optional_import("PIL", name="Image")
-ImageDraw, _ = optional_import("PIL", name="ImageDraw")
-ImageFont, _ = optional_import("PIL", name="ImageFont")
+ImageDraw, _ = optional_import("PIL.ImageDraw")
+ImageFont, _ = optional_import("PIL.ImageFont")
 
 
 class Llama3VILAInferenceOperator(Operator):