Add Model Parameters (open-edge-platform#142)

* Add new model parameters: batch size, nireq, and inference interval
* Add input fields to capture these parameter values from the user
* Updated relevant pipeline to utilize the new inputs

Co-authored-by: Nicolas Oliver <[email protected]>

Author: basakcap

tools/visual-pipeline-and-platform-evaluation-tool/app.py

@@ -422,6 +422,35 @@ def create_interface():
         value=preferred_device,
     )
 
+    # Batch size
+    batch_size = gr.Slider(
+        minimum=0,
+        maximum=1024,
+        value=0,
+        step=1,
+        label="Batch Size",
+        interactive=True,
+    )
+
+    # Inference interval
+    inference_interval = gr.Slider(
+        minimum=1,
+        maximum=1800,
+        value=1,
+        step=1,
+        label="Inference Interval",
+        interactive=True,
+    )
+
+    # Number of inference requests (nireq)
+    nireq = gr.Slider(
+        minimum=0,
+        maximum=1024,
+        value=0,
+        step=1,
+        label="Number of Inference Requests (nireq)",
+        interactive=True,
+    )
     # This elements are not used in the current version of the app
     # # Object classification accordion
     # object_classification_accordion = gr.Accordion(
@@ -491,9 +520,13 @@ def on_run(
                     # This elements are not used in the current version of the app
                     # object_classification_model,
                     # object_classification_device,
+                    batch_size,
+                    inference_interval,
+                    nireq,
                     input_video_player,
                 ):
 
+
                     random_string = "".join(
                         random.choices(string.ascii_lowercase + string.digits, k=6)
                     )
@@ -508,6 +541,9 @@ def on_run(
 
                     param_grid = {
                         "object_detection_device": object_detection_device.split(", "),
+                        "batch_size": [batch_size],
+                        "inference_interval": [inference_interval],
+                        "nireq": [nireq],
                         # This elements are not used in the current version of the app
                         # "vehicle_classification_device": object_classification_device.split(
                         #     ", "
@@ -621,6 +657,9 @@ def on_run(
                         # This elements are not used in the current version of the app
                         # object_classification_model,
                         # object_classification_device,
+                        batch_size,
+                        inference_interval,
+                        nireq,
                         input_video_player,
                     ],
                     outputs=[output_video_player, cpu_metrics_plot, gpu_time_series_plot],
@@ -643,6 +682,9 @@ def on_run(
                 with object_detection_accordion.render():
                     object_detection_model.render()
                     object_detection_device.render()
+                    batch_size.render()
+                    inference_interval.render()
+                    nireq.render()
 
                 # This elements are not used in the current version of the app
                 # with object_classification_accordion.render():

tools/visual-pipeline-and-platform-evaluation-tool/pipeline.py

@@ -163,10 +163,12 @@ def __init__(self):
             "gvadetect "
             "  model={OBJECT_DETECTION_MODEL_PATH} "
             "  model-proc={OBJECT_DETECTION_MODEL_PROC} "
-            "  inference-interval=3 "
             "  model-instance-id=detect0 "
             "  pre-process-backend={object_detection_pre_process_backend} "
-            "  device={object_detection_device} ! "
+            "  device={object_detection_device} "
+            "  batch-size={batch_size} "  
+            "  inference-interval={inference_interval} " 
+            "  nireq={nireq} ! "  
             "queue2 "
             "  max-size-buffers=0 "
             "  max-size-bytes=0 "
@@ -280,7 +282,6 @@ def evaluate(
                 postprocessing=_postprocessing_element
             )
 
-
         # Evaluate the pipeline
         return "gst-launch-1.0 -q " + compositor + " " + streams
 
@@ -294,13 +295,25 @@ def evaluate(
     print(
         "Evaluate:",
         pipeline.evaluate(
-            {
+            constant = {
                 "VIDEO_OUTPUT_PATH": "output.mp4",
                 "VIDEO_PATH": "input.mp4",
                 "OBJECT_DETECTION_MODEL_PATH": "model.xml",
                 "OBJECT_DETECTION_MODEL_PROC": "model_proc.xml",
             },
-            {"object_detection_device": "CPU"},
-            2,
+            parameters = {
+                "object_detection_device": "CPU",
+                "batch_size": 16,  
+                "inference_interval": 2, 
+                "nireq": 4,  
+            },
+            regular_channels = 2,
+            inference_channels = 1,
+            elements = [
+                "compositor",
+                "x264enc",
+                "decodebin",
+                "videoscale"
+            ]
         ),
     )