utilities for video streaming

clarifai/client/model.py

@@ -424,14 +424,14 @@ def predict(self,
       raise UserError(f"Too many inputs. Max is {MAX_MODEL_PREDICT_INPUTS}."
                      )  # TODO Use Chunker for inputs len > 128
 
-    self._override_model_version(inference_params, output_config)
+    model_info = self._get_model_info_for_inference(inference_params, output_config)
     request = service_pb2.PostModelOutputsRequest(
         user_app_id=self.user_app_id,
         model_id=self.id,
         version_id=self.model_version.id,
         inputs=inputs,
         runner_selector=runner_selector,
-        model=self.model_info)
+        model=model_info)
 
     start_time = time.time()
     backoff_iterator = BackoffIterator(10)
@@ -704,14 +704,14 @@ def generate(self,
       raise UserError(f"Too many inputs. Max is {MAX_MODEL_PREDICT_INPUTS}."
                      )  # TODO Use Chunker for inputs len > 128
 
-    self._override_model_version(inference_params, output_config)
+    model_info = self._get_model_info_for_inference(inference_params, output_config)
     request = service_pb2.PostModelOutputsRequest(
         user_app_id=self.user_app_id,
         model_id=self.id,
         version_id=self.model_version.id,
         inputs=inputs,
         runner_selector=runner_selector,
-        model=self.model_info)
+        model=model_info)
 
     start_time = time.time()
     backoff_iterator = BackoffIterator(10)
@@ -922,15 +922,16 @@ def generate_by_url(self,
         inference_params=inference_params,
         output_config=output_config)
 
-  def _req_iterator(self, input_iterator: Iterator[List[Input]], runner_selector: RunnerSelector):
+  def _req_iterator(self, input_iterator: Iterator[List[Input]], runner_selector: RunnerSelector,
+                    model_info: resources_pb2.Model):
     for inputs in input_iterator:
       yield service_pb2.PostModelOutputsRequest(
           user_app_id=self.user_app_id,
           model_id=self.id,
           version_id=self.model_version.id,
           inputs=inputs,
           runner_selector=runner_selector,
-          model=self.model_info)
+          model=model_info)
 
   def stream(self,
              inputs: Iterator[List[Input]],
@@ -954,8 +955,8 @@ def stream(self,
     # if not isinstance(inputs, Iterator[List[Input]]):
     #   raise UserError('Invalid inputs, inputs must be a iterator of list of Input objects.')
 
-    self._override_model_version(inference_params, output_config)
-    request = self._req_iterator(inputs, runner_selector)
+    model_info = self._get_model_info_for_inference(inference_params, output_config)
+    request = self._req_iterator(inputs, runner_selector, model_info)
 
     start_time = time.time()
     backoff_iterator = BackoffIterator(10)
@@ -1168,8 +1169,54 @@ def input_generator():
         inference_params=inference_params,
         output_config=output_config)
 
-  def _override_model_version(self, inference_params: Dict = {}, output_config: Dict = {}) -> None:
-    """Overrides the model version.
+  def stream_by_video_file(self,
+                           filepath: str,
+                           input_type: str = 'video',
+                           compute_cluster_id: str = None,
+                           nodepool_id: str = None,
+                           deployment_id: str = None,
+                           user_id: str = None,
+                           inference_params: Dict = {},
+                           output_config: Dict = {}):
+    """
+    Stream the model output based on the given video file.
+
+    Converts the video file to a streamable format, streams as bytes to the model,
+    and streams back the model outputs.
+
+    Args:
+        filepath (str): The filepath to predict.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        compute_cluster_id (str): The compute cluster ID to use for the model.
+        nodepool_id (str): The nodepool ID to use for the model.
+        deployment_id (str): The deployment ID to use for the model.
+        inference_params (dict): The inference params to override.
+        output_config (dict): The output config to override.
+    """
+
+    if not os.path.isfile(filepath):
+      raise UserError('Invalid filepath.')
+
+    # TODO check if the file is streamable already
+
+    # Convert the video file to a streamable format
+    # TODO this conversion can offset the start time by a little bit; we should account for this
+    # by getting the original start time ffprobe and either sending that to the model so it can adjust
+    # with the ts of the first frame (too fragile to do all of this adjustment in the client input stream)
+    # or by adjusting the timestamps in the output stream
+    from clarifai.runners.utils import video_utils
+    stream = video_utils.recontain_as_streamable(filepath)
+
+    # TODO accumulate reads to fill the chunk size
+    chunk_size = 1024 * 1024  # 1 MB
+    chunk_iterator = iter(lambda: stream.read(chunk_size), b'')
+
+    return self.stream_by_bytes(chunk_iterator, input_type, compute_cluster_id, nodepool_id,
+                                deployment_id, user_id, inference_params, output_config)
+
+  def _get_model_info_for_inference(self, inference_params: Dict = {},
+                                    output_config: Dict = {}) -> None:
+    """Gets the model_info with modified inference params and output config.
 
     Args:
         inference_params (dict): The inference params to override.
@@ -1179,13 +1226,12 @@ def _override_model_version(self, inference_params: Dict = {}, output_config: Di
           select_concepts (list[Concept]): The concepts to select.
           sample_ms (int): The number of milliseconds to sample.
     """
-    params = Struct()
-    if inference_params is not None:
-      params.update(inference_params)
-
-    self.model_info.model_version.output_info.CopyFrom(
-        resources_pb2.OutputInfo(
-            output_config=resources_pb2.OutputConfig(**output_config), params=params))
+    model_info = resources_pb2.Model()
+    model_info.CopyFrom(self.model_info)
+    model_info.model_version.output_info.params = inference_params
+    model_info.model_version.output_info.output_config.CopyFrom(
+        resources_pb2.OutputConfig(**output_config))
+    return model_info
 
   def _list_concepts(self) -> List[str]:
     """Lists all the concepts for the model type.

clarifai/runners/models/base_typed_model.py

@@ -6,6 +6,9 @@
 from clarifai_grpc.grpc.api.service_pb2 import PostModelOutputsRequest
 from google.protobuf import json_format
 
+from clarifai.runners.utils.stream_utils import readahead
+from clarifai.runners.utils.url_fetcher import ensure_urls_downloaded
+
 from ..utils.data_handler import InputDataHandler, OutputDataHandler
 from .model_class import ModelClass
 
@@ -46,12 +49,16 @@ def convert_output_to_proto(self, outputs: list):
 
   def predict_wrapper(
       self, request: service_pb2.PostModelOutputsRequest) -> service_pb2.MultiOutputResponse:
+    if self.download_request_urls:
+      ensure_urls_downloaded(request)
     list_dict_input, inference_params = self.parse_input_request(request)
     outputs = self.predict(list_dict_input, inference_parameters=inference_params)
     return self.convert_output_to_proto(outputs)
 
   def generate_wrapper(
       self, request: PostModelOutputsRequest) -> Iterator[service_pb2.MultiOutputResponse]:
+    if self.download_request_urls:
+      ensure_urls_downloaded(request)
     list_dict_input, inference_params = self.parse_input_request(request)
     outputs = self.generate(list_dict_input, inference_parameters=inference_params)
     for output in outputs:
@@ -64,11 +71,13 @@ def _preprocess_stream(
       input_data, _ = self.parse_input_request(req)
       yield input_data
 
-  def stream_wrapper(self, request: Iterator[PostModelOutputsRequest]
+  def stream_wrapper(self, request_iterator: Iterator[PostModelOutputsRequest]
                     ) -> Iterator[service_pb2.MultiOutputResponse]:
-    first_request = next(request)
+    if self.download_request_urls:
+      request_iterator = readahead(map(ensure_urls_downloaded, request_iterator))
+    first_request = next(request_iterator)
     _, inference_params = self.parse_input_request(first_request)
-    request_iterator = itertools.chain([first_request], request)
+    request_iterator = itertools.chain([first_request], request_iterator)
     outputs = self.stream(self._preprocess_stream(request_iterator), inference_params)
     for output in outputs:
       yield self.convert_output_to_proto(output)

clarifai/runners/models/model_class.py

@@ -3,23 +3,41 @@
 
 from clarifai_grpc.grpc.api import service_pb2
 
+from clarifai.runners.utils.stream_utils import readahead
+from clarifai.runners.utils.url_fetcher import ensure_urls_downloaded
+
 
 class ModelClass(ABC):
 
+  download_request_urls = True
+
   def predict_wrapper(
       self, request: service_pb2.PostModelOutputsRequest) -> service_pb2.MultiOutputResponse:
     """This method is used for input/output proto data conversion"""
+    # Download any urls that are not already bytes.
+    if self.download_request_urls:
+      ensure_urls_downloaded(request)
+
     return self.predict(request)
 
   def generate_wrapper(self, request: service_pb2.PostModelOutputsRequest
                       ) -> Iterator[service_pb2.MultiOutputResponse]:
     """This method is used for input/output proto data conversion and yield outcome"""
+    # Download any urls that are not already bytes.
+    if self.download_request_urls:
+      ensure_urls_downloaded(request)
+
     return self.generate(request)
 
-  def stream_wrapper(self, request: service_pb2.PostModelOutputsRequest
+  def stream_wrapper(self, request_stream: Iterator[service_pb2.PostModelOutputsRequest]
                     ) -> Iterator[service_pb2.MultiOutputResponse]:
     """This method is used for input/output proto data conversion and yield outcome"""
-    return self.stream(request)
+
+    # Download any urls that are not already bytes.
+    if self.download_request_urls:
+      request_stream = readahead(map(ensure_urls_downloaded, request_stream))
+
+    return self.stream(request_stream)
 
   @abstractmethod
   def load_model(self):

clarifai/runners/models/model_runner.py

@@ -5,7 +5,6 @@
 
 from clarifai_protocol import BaseRunner
 from clarifai_protocol.utils.health import HealthProbeRequestHandler
-from ..utils.url_fetcher import ensure_urls_downloaded
 
 from .model_class import ModelClass
 
@@ -79,7 +78,6 @@ def runner_item_predict(self,
     if not runner_item.HasField('post_model_outputs_request'):
       raise Exception("Unexpected work item type: {}".format(runner_item))
     request = runner_item.post_model_outputs_request
-    ensure_urls_downloaded(request)
 
     resp = self.model.predict_wrapper(request)
     successes = [o.status.code == status_code_pb2.SUCCESS for o in resp.outputs]
@@ -109,7 +107,6 @@ def runner_item_generate(
     if not runner_item.HasField('post_model_outputs_request'):
       raise Exception("Unexpected work item type: {}".format(runner_item))
     request = runner_item.post_model_outputs_request
-    ensure_urls_downloaded(request)
 
     for resp in self.model.generate_wrapper(request):
       successes = []
@@ -169,5 +166,4 @@ def pmo_iterator(runner_item_iterator):
   for runner_item in runner_item_iterator:
     if not runner_item.HasField('post_model_outputs_request'):
       raise Exception("Unexpected work item type: {}".format(runner_item))
-    ensure_urls_downloaded(runner_item.post_model_outputs_request)
     yield runner_item.post_model_outputs_request

clarifai/runners/models/model_servicer.py

@@ -1,11 +1,8 @@
-from itertools import tee
 from typing import Iterator
 
 from clarifai_grpc.grpc.api import service_pb2, service_pb2_grpc
 from clarifai_grpc.grpc.api.status import status_code_pb2, status_pb2
 
-from ..utils.url_fetcher import ensure_urls_downloaded
-
 
 class ModelServicer(service_pb2_grpc.V2Servicer):
   """
@@ -27,9 +24,6 @@ def PostModelOutputs(self, request: service_pb2.PostModelOutputsRequest,
     returns an output.
     """
 
-    # Download any urls that are not already bytes.
-    ensure_urls_downloaded(request)
-
     try:
       return self.model.predict_wrapper(request)
     except Exception as e:
@@ -46,9 +40,6 @@ def GenerateModelOutputs(self, request: service_pb2.PostModelOutputsRequest,
     This is the method that will be called when the servicer is run. It takes in an input and
     returns an output.
     """
-    # Download any urls that are not already bytes.
-    ensure_urls_downloaded(request)
-
     try:
       return self.model.generate_wrapper(request)
     except Exception as e:
@@ -66,15 +57,8 @@ def StreamModelOutputs(self,
     This is the method that will be called when the servicer is run. It takes in an input and
     returns an output.
     """
-    # Duplicate the iterator
-    request, request_copy = tee(request)
-
-    # Download any urls that are not already bytes.
-    for req in request:
-      ensure_urls_downloaded(req)
-
     try:
-      return self.model.stream_wrapper(request_copy)
+      return self.model_class.stream_wrapper(request)
     except Exception as e:
       yield service_pb2.MultiOutputResponse(status=status_pb2.Status(
           code=status_code_pb2.MODEL_PREDICTION_FAILED,