Fix sample image input data type issue.

quic-zhanweiw · quic-zhanweiw · commit 8a058c645ccb · 2026-01-13T17:49:56.000+08:00
diff --git a/samples/linux/python/convnext_base/convnext_base.py b/samples/linux/python/convnext_base/convnext_base.py
@@ -145,7 +145,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = convnext_base.Inference([image])
+    output_data = convnext_base.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/convnext_tiny/convnext_tiny.py b/samples/linux/python/convnext_tiny/convnext_tiny.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = convnext_tiny.Inference([image])
+    output_data = convnext_tiny.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/efficientnet_b0/efficientnet_b0.py b/samples/linux/python/efficientnet_b0/efficientnet_b0.py
@@ -145,7 +145,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = efficientnet_b0.Inference([image])
+    output_data = efficientnet_b0.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/efficientnet_b4/efficientnet_b4.py b/samples/linux/python/efficientnet_b4/efficientnet_b4.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = efficientnet_b4.Inference([image])
+    output_data = efficientnet_b4.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/efficientnet_v2_s/efficientnet_v2_s.py b/samples/linux/python/efficientnet_v2_s/efficientnet_v2_s.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = efficientnet_v2_s.Inference([image])
+    output_data = efficientnet_v2_s.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/googlenet/googlenet.py b/samples/linux/python/googlenet/googlenet.py
@@ -159,7 +159,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
         # Run the inference.
-    output_data = googlenet.Inference([image])
+    output_data = googlenet.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/inception_v3/inception_v3.py b/samples/linux/python/inception_v3/inception_v3.py
@@ -146,7 +146,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = inceptionV3.Inference([image])
+    output_data = inceptionV3.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/levit/levit.py b/samples/linux/python/levit/levit.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = levit.Inference([image])
+    output_data = levit.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/quicksrnetmedium/quicksrnetmedium.py b/samples/linux/python/quicksrnetmedium/quicksrnetmedium.py
@@ -116,7 +116,7 @@ def Inference(input_image_path, output_image_path, show_image = True):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_image = quicksrnetmedium.Inference([image])
+    output_image = quicksrnetmedium.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/real_esrgan_general_x4v3/real_esrgan_general_x4v3.py b/samples/linux/python/real_esrgan_general_x4v3/real_esrgan_general_x4v3.py
@@ -121,7 +121,7 @@ def Inference(input_image_path, output_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_image = realesrgan.Inference([image])
+    output_image = realesrgan.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/real_esrgan_x4plus/real_esrgan_x4plus.py b/samples/linux/python/real_esrgan_x4plus/real_esrgan_x4plus.py
@@ -109,7 +109,7 @@ def Inference(input_image_path, output_image_path, show_image = True):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_image = realesrgan.Inference([image])
+    output_image = realesrgan.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/regnet/regnet.py b/samples/linux/python/regnet/regnet.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = regnet.Inference([image])
+    output_data = regnet.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/sesr_m5/sesr_m5.py b/samples/linux/python/sesr_m5/sesr_m5.py
@@ -113,7 +113,7 @@ def Inference(input_image_path, output_image_path, show_image = True):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_image = sesr_m5.Inference([image])
+    output_image = sesr_m5.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/shufflenet_v2/shufflenet_v2.py b/samples/linux/python/shufflenet_v2/shufflenet_v2.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = shufflenet_v2.Inference([image])
+    output_data = shufflenet_v2.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/squeezenet1_1/squeezenet1_1.py b/samples/linux/python/squeezenet1_1/squeezenet1_1.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = squeezenet1_1.Inference([image])
+    output_data = squeezenet1_1.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/vit/vit.py b/samples/linux/python/vit/vit.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = vit.Inference([image])
+    output_data = vit.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/wideresnet50/wideresnet50.py b/samples/linux/python/wideresnet50/wideresnet50.py
@@ -144,7 +144,7 @@ def Inference(input_image_path):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_data = wideresnet50.Inference([image])
+    output_data = wideresnet50.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/xlsr/xlsr.py b/samples/linux/python/xlsr/xlsr.py
@@ -111,7 +111,7 @@ def Inference(input_image_path, output_image_path, show_image = True):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    output_image = xlsr.Inference([image])
+    output_image = xlsr.Inference(image)
 
     # Reset the HTP.
     PerfProfile.RelPerfProfileGlobal()
diff --git a/samples/linux/python/yolov8_det/yolov8_det.py b/samples/linux/python/yolov8_det/yolov8_det.py
@@ -347,7 +347,7 @@ def Inference(input_image_path, output_image_path, show_image = True):
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
     # Run the inference.
-    model_output = yolov8.Inference([image])
+    model_output = yolov8.Inference(image)
     if SOC_ID == "6490":
         pred_boxes = torch.tensor(model_output[2].reshape(1, -1, 4))
         pred_scores = torch.tensor(model_output[0].reshape(1, -1))
