Add support for MMD models in import model pipeline

assets/training/model_management/components/convert_model_to_mlflow/spec.yaml

@@ -1,7 +1,7 @@
 $schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
 
 name: convert_model_to_mlflow
-version: 0.0.33
+version: 0.0.34
 type: command
 
 is_deterministic: True
@@ -19,7 +19,7 @@ command: |
   pip_pkg_str="${pip_pkgs[*]}"
   if [[ -n "$pip_pkg_str" ]]; then echo "Installing $pip_pkg_str"; pip install $pip_pkg_str; echo "pip installation completed. For any installation error please check above logs"; fi;
   echo "Running model conversion ... "
-  python -u run_model_preprocess.py $[[--model-id ${{inputs.model_id}}]] $[[--task-name ${{inputs.task_name}}]] $[[--model-download-metadata ${{inputs.model_download_metadata}}]] $[[--license-file-path ${{inputs.license_file_path}}]] $[[--hf-config-args "${{inputs.hf_config_args}}"]] $[[--hf-tokenizer-args "${{inputs.hf_tokenizer_args}}"]] $[[--hf-model-args "${{inputs.hf_model_args}}"]] $[[--hf-pipeline-args "${{inputs.hf_pipeline_args}}"]] $[[--hf-config-class ${{inputs.hf_config_class}}]] $[[--hf-model-class ${{inputs.hf_model_class}}]] $[[--hf-tokenizer-class ${{inputs.hf_tokenizer_class}}]] $[[--hf-use-experimental-features ${{inputs.hf_use_experimental_features}}]] $[[--extra-pip-requirements "${{inputs.extra_pip_requirements}}"]] $[[--inference-base-image "${{inputs.inference_base_image}}"]] --vllm-enabled ${{inputs.vllm_enabled}} --model-framework ${{inputs.model_framework}} --model-path ${{inputs.model_path}} --mlflow-model-output-dir ${{outputs.mlflow_model_folder}} --model-flavor ${{inputs.model_flavor}}
+  python -u run_model_preprocess.py $[[--model-id ${{inputs.model_id}}]] $[[--task-name ${{inputs.task_name}}]] $[[--model-download-metadata ${{inputs.model_download_metadata}}]] $[[--license-file-path ${{inputs.license_file_path}}]] $[[--hf-config-args "${{inputs.hf_config_args}}"]] $[[--hf-tokenizer-args "${{inputs.hf_tokenizer_args}}"]] $[[--hf-model-args "${{inputs.hf_model_args}}"]] $[[--hf-pipeline-args "${{inputs.hf_pipeline_args}}"]] $[[--hf-config-class ${{inputs.hf_config_class}}]] $[[--hf-model-class ${{inputs.hf_model_class}}]] $[[--hf-tokenizer-class ${{inputs.hf_tokenizer_class}}]] $[[--hf-use-experimental-features ${{inputs.hf_use_experimental_features}}]] $[[--extra-pip-requirements "${{inputs.extra_pip_requirements}}"]] $[[--inference-base-image "${{inputs.inference_base_image}}"]] --vllm-enabled ${{inputs.vllm_enabled}} --model-framework ${{inputs.model_framework}} $[[--model-path "${{inputs.model_path}}"]] $[[--model-path-mmd "${{inputs.model_path_mmd}}"]] --mlflow-model-output-dir ${{outputs.mlflow_model_folder}} --model-flavor ${{inputs.model_flavor}}
   echo "Completed model conversion ... "
 
 inputs:
@@ -156,7 +156,13 @@ inputs:
     type: uri_folder
     description: Path to the model.
     mode: ro_mount
-    optional: false
+    optional: true
+
+  model_path_mmd:
+    type: uri_folder
+    description: Path to the MMD model.
+    mode: ro_mount
+    optional: true
 
   license_file_path:
     type: uri_file

assets/training/model_management/components/import_model/spec.yaml

@@ -4,7 +4,7 @@ type: pipeline
 name: import_model
 display_name: Import model
 description: Import a model into a workspace or a registry
-version: 0.0.39
+version: 0.0.40
 
 # Pipeline inputs
 inputs:
@@ -270,8 +270,23 @@ jobs:
       model_output:
         type: uri_folder
 
+  mmdetection_image_objectdetection_instancesegmentation_model_import:
+    component: azureml:mmdetection_image_objectdetection_instancesegmentation_model_import/versions/0.0.19
+    compute: ${{parent.inputs.compute}}
+    resources:
+      instance_type: '${{parent.inputs.instance_type}}'
+    identity:
+      type: user_identity
+    inputs:
+      model_family: 'MmDetectionImage'
+      model_name: ${{parent.inputs.model_id}}
+      download_from_source: False
+    outputs:
+      output_dir:
+        type: uri_file
+
   convert_model_to_mlflow:
-    component: azureml:convert_model_to_mlflow:0.0.33
+    component: azureml:convert_model_to_mlflow:0.0.34
     compute: ${{parent.inputs.compute}}
     resources:
       instance_type: '${{parent.inputs.instance_type}}'
@@ -284,6 +299,7 @@ jobs:
       license_file_path: ${{parent.inputs.license_file_path}}
       model_framework: ${{parent.inputs.model_framework}}
       model_download_metadata: ${{parent.jobs.download_model.outputs.model_download_metadata}}
+      model_path_mmd:  ${{parent.jobs.mmdetection_image_objectdetection_instancesegmentation_model_import.outputs.output_dir}}
       model_path: ${{parent.jobs.download_model.outputs.model_output}}
       hf_config_args: ${{parent.inputs.hf_config_args}}
       hf_tokenizer_args:  ${{parent.inputs.hf_tokenizer_args}}

assets/training/model_management/src/run_model_preprocess.py

@@ -73,7 +73,8 @@ def _get_parser():
         required=False,
         help="Model download details",
     )
-    parser.add_argument("--model-path", type=Path, required=True, help="Model input path")
+    parser.add_argument("--model-path", type=Path, required=False, help="Model input path")
+    parser.add_argument("--model-path-mmd", type=Path, required=False, help="MMD Model input path")
     parser.add_argument("--license-file-path", type=Path, required=False, help="License file path")
     parser.add_argument(
         "--mlflow-model-output-dir",
@@ -107,7 +108,7 @@ def run():
     inference_base_image = args.inference_base_image
 
     model_download_metadata_path = args.model_download_metadata
-    model_path = args.model_path
+    model_path = args.model_path_mmd if model_framework == 'MMLab' else args.model_path
     mlflow_model_output_dir = args.mlflow_model_output_dir
     license_file_path = args.license_file_path
     TRUST_CODE_KEY = "trust_remote_code=True"