Add extra params to MLModel (#783)

src/viam/services/mlmodel/client.py

@@ -1,11 +1,12 @@
-from typing import Dict, Optional
+from typing import Dict, Mapping, Optional
 
 from grpclib.client import Channel
 from numpy.typing import NDArray
 
 from viam.proto.service.mlmodel import InferRequest, InferResponse, MetadataRequest, MetadataResponse, MLModelServiceStub
 from viam.resource.rpc_client_base import ReconfigurableResourceRPCClientBase
 from viam.services.mlmodel.utils import flat_tensors_to_ndarrays, ndarrays_to_flat_tensors
+from viam.utils import ValueTypes, dict_to_struct
 
 from .mlmodel import Metadata, MLModel
 
@@ -16,14 +17,21 @@ def __init__(self, name: str, channel: Channel):
         self.client = MLModelServiceStub(channel)
         super().__init__(name)
 
-    async def infer(self, input_tensors: Dict[str, NDArray], *, timeout: Optional[float] = None, **kwargs) -> Dict[str, NDArray]:
+    async def infer(
+        self,
+        input_tensors: Dict[str, NDArray],
+        *,
+        extra: Optional[Mapping[str, ValueTypes]] = None,
+        timeout: Optional[float] = None,
+        **kwargs,
+    ) -> Dict[str, NDArray]:
         md = kwargs.get("metadata", self.Metadata()).proto
-        request = InferRequest(name=self.name, input_tensors=ndarrays_to_flat_tensors(input_tensors))
+        request = InferRequest(name=self.name, input_tensors=ndarrays_to_flat_tensors(input_tensors), extra=dict_to_struct(extra))
         response: InferResponse = await self.client.Infer(request, timeout=timeout, metadata=md)
         return flat_tensors_to_ndarrays(response.output_tensors)
 
-    async def metadata(self, *, timeout: Optional[float] = None, **kwargs) -> Metadata:
+    async def metadata(self, *, extra: Optional[Mapping[str, ValueTypes]] = None, timeout: Optional[float] = None, **kwargs) -> Metadata:
         md = kwargs.get("metadata", self.Metadata()).proto
-        request = MetadataRequest(name=self.name)
+        request = MetadataRequest(name=self.name, extra=dict_to_struct(extra))
         response: MetadataResponse = await self.client.Metadata(request, timeout=timeout, metadata=md)
         return response.metadata

src/viam/services/mlmodel/mlmodel.py

@@ -1,10 +1,11 @@
 import abc
-from typing import Dict, Final, Optional
+from typing import Dict, Final, Optional, Mapping
 
 from numpy.typing import NDArray
 
 from viam.proto.service.mlmodel import Metadata
 from viam.resource.types import RESOURCE_NAMESPACE_RDK, RESOURCE_TYPE_SERVICE, Subtype
+from viam.utils import ValueTypes
 
 from ..service_base import ServiceBase
 
@@ -25,7 +26,13 @@ class MLModel(ServiceBase):
     )
 
     @abc.abstractmethod
-    async def infer(self, input_tensors: Dict[str, NDArray], *, timeout: Optional[float]) -> Dict[str, NDArray]:
+    async def infer(
+        self,
+        input_tensors: Dict[str, NDArray],
+        *,
+        extra: Optional[Mapping[str, ValueTypes]] = None,
+        timeout: Optional[float] = None,
+    ) -> Dict[str, NDArray]:
         """Take an already ordered input tensor as an array, make an inference on the model, and return an output tensor map.
 
         ::
@@ -50,7 +57,7 @@ async def infer(self, input_tensors: Dict[str, NDArray], *, timeout: Optional[fl
         ...
 
     @abc.abstractmethod
-    async def metadata(self, *, timeout: Optional[float]) -> Metadata:
+    async def metadata(self, *, extra: Optional[Mapping[str, ValueTypes]] = None, timeout: Optional[float] = None) -> Metadata:
         """Get the metadata (such as name, type, expected tensor/array shape, inputs, and outputs) associated with the ML model.
 
         ::

src/viam/services/mlmodel/service.py

@@ -3,6 +3,7 @@
 from viam.proto.service.mlmodel import InferRequest, InferResponse, MetadataRequest, MetadataResponse, MLModelServiceBase
 from viam.resource.rpc_service_base import ResourceRPCServiceBase
 from viam.services.mlmodel.utils import flat_tensors_to_ndarrays, ndarrays_to_flat_tensors
+from viam.utils import struct_to_dict
 
 from .mlmodel import MLModel
 
@@ -19,8 +20,9 @@ async def Infer(self, stream: Stream[InferRequest, InferResponse]) -> None:
         assert request is not None
         name = request.name
         mlmodel = self.get_resource(name)
+        extra = struct_to_dict(request.extra)
         timeout = stream.deadline.time_remaining() if stream.deadline else None
-        output_tensors = await mlmodel.infer(input_tensors=flat_tensors_to_ndarrays(request.input_tensors), timeout=timeout)
+        output_tensors = await mlmodel.infer(input_tensors=flat_tensors_to_ndarrays(request.input_tensors), extra=extra, timeout=timeout)
         response = InferResponse(output_tensors=ndarrays_to_flat_tensors(output_tensors))
         await stream.send_message(response)
 
@@ -29,7 +31,8 @@ async def Metadata(self, stream: Stream[MetadataRequest, MetadataResponse]) -> N
         assert request is not None
         name = request.name
         mlmodel = self.get_resource(name)
+        extra = struct_to_dict(request.extra)
         timeout = stream.deadline.time_remaining() if stream.deadline else None
-        metadata = await mlmodel.metadata(timeout=timeout)
+        metadata = await mlmodel.metadata(extra=extra, timeout=timeout)
         response = MetadataResponse(metadata=metadata)
         await stream.send_message(response)

tests/mocks/services.py

@@ -515,13 +515,19 @@ def __init__(self, name: str):
 
         super().__init__(name)
 
-    async def infer(self, input_tensors: Dict[str, NDArray], *, timeout: Optional[float] = None) -> Dict[str, NDArray]:
+    async def infer(
+        self,
+        input_tensors: Dict[str, NDArray],
+        *,
+        extra: Optional[Mapping[str, ValueTypes]] = None,
+        timeout: Optional[float] = None,
+    ) -> Dict[str, NDArray]:
         self.timeout = timeout
         request_data = ndarrays_to_flat_tensors(input_tensors)
         response_data = flat_tensors_to_ndarrays(request_data)
         return response_data
 
-    async def metadata(self, *, timeout: Optional[float] = None) -> Metadata:
+    async def metadata(self, *, extra: Optional[Mapping[str, ValueTypes]] = None, timeout: Optional[float] = None) -> Metadata:
         self.timeout = timeout
         return self.META