Update sample code

samples/python/README.md

@@ -57,5 +57,8 @@ python stable_diffusion_v2_1\stable_diffusion_v2_1.py --prompt "spectacular view
 | riffusion  | 2.24 | python riffusion\riffusion.py --prompt "the prompt string ..." |
 | real_esrgan_x4plus  | 2.28 | python real_esrgan_x4plus\real_esrgan_x4plus.py |
 | real_esrgan_general_x4v3  | 2.28 | python real_esrgan_general_x4v3\real_esrgan_general_x4v3.py |
+| inception_v3  | 2.28 | python inception_v3\inception_v3.py |
+| yolov8_det  | 2.28 | python yolov8_det\yolov8_det.py |
+| unet_segmentation  | 2.28 | python unet_segmentation\unet_segmentation.py |
 
 *More models will be supported soon!*

samples/python/inception_v3/README.md

@@ -5,7 +5,7 @@ This is sample code for using AppBuilder to load inception_v3 QNN model to HTP a
 It can also be used as a backbone in building more complex models for specific use cases.
 
 ## Setup AppBuilder environment and prepare QNN SDK libraries by referring to the links below: 
-https://github.com/quic/ai-engine-direct-helper/blob/main/README.md
+https://github.com/quic/ai-engine-direct-helper/blob/main/README.md <br>
 https://github.com/quic/ai-engine-direct-helper/blob/main/Docs/User_Guide.md
 
 Copy the QNN libraries from QNN SDK to below path:

samples/python/inception_v3/inception_v3.py

@@ -3,10 +3,13 @@
 # SPDX-License-Identifier: BSD-3-Clause
 # ---------------------------------------------------------------------
 
+import sys
 import os
+sys.path.append(".")
+sys.path.append("..")
+import utils.install as install
 import cv2
 import numpy as np
-
 import torch
 import torchvision.transforms as transforms
 from PIL import Image
@@ -16,12 +19,33 @@
 
 ####################################################################
 
+MODEL_ID = "mmxeyyvyn"
+MODEL_NAME = "inception_v3"
+MODEL_HELP_URL = "https://github.com/quic/ai-engine-direct-helper/tree/main/samples/python/" + MODEL_NAME + "#" + MODEL_NAME + "-qnn-models"
+IMAGENET_CLASSES_URL = "https://raw.githubusercontent.com/pytorch/hub/refs/heads/master/imagenet_classes.txt"
+IMAGENET_CLASSES_FILE = "imagenet_classes.txt"
+IMAGE_SIZE = 224
+
+####################################################################
+
+execution_ws = os.getcwd()
+qnn_dir = execution_ws + "\\qai_libs"
+
+if not MODEL_NAME in execution_ws:
+    execution_ws = execution_ws + "\\" + MODEL_NAME
+
+model_dir = execution_ws + "\\models"
+madel_path = model_dir + "\\" + MODEL_NAME + ".bin"
+imagenet_classes_path = model_dir + "\\" + IMAGENET_CLASSES_FILE
+
+####################################################################
+
 inceptionV3 = None
 
 def preprocess_PIL_image(image: Image) -> torch.Tensor:
     preprocess = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(224),
+        transforms.Resize(IMAGE_SIZE),
+        transforms.CenterCrop(IMAGE_SIZE),
         transforms.ToTensor(),
     ])
 
@@ -31,7 +55,7 @@ def preprocess_PIL_image(image: Image) -> torch.Tensor:
 
 def post_process(probabilities, output):
     # Read the categories
-    with open("imagenet_classes.txt", "r") as f:
+    with open(imagenet_classes_path, "r") as f:
         categories = [s.strip() for s in f.readlines()]
     # Show top categories per image
     top5_prob, top5_catid = torch.topk(probabilities, 5)
@@ -46,22 +70,36 @@ def Inference(self, input_data):
         output_data = super().Inference(input_datas)[0]        
         return output_data
 
+def model_download():
+    ret = True
+
+    if not os.path.exists(imagenet_classes_path):
+        ret = install.download_url(IMAGENET_CLASSES_URL, imagenet_classes_path)
+
+    desc = f"Downloading {MODEL_NAME} model... "
+    fail = f"\nFailed to download {MODEL_NAME} model. Please prepare the model according to the steps in below link:\n{MODEL_HELP_URL}"
+    ret = install.download_qai_hubmodel(MODEL_ID, madel_path, desc=desc, fail=fail)
+
+    if not ret:
+        exit()
+
 def Init():
     global inceptionV3
 
+    model_download()
+
     # Config AppBuilder environment.
     QNNConfig.Config(os.getcwd() + "\\qai_libs", Runtime.HTP, LogLevel.WARN, ProfilingLevel.BASIC)
 
     # Instance for InceptionV3 objects.
-    inceptionV3_model = "models\\inception_v3.bin"
-    inceptionV3 = InceptionV3("inceptionV3", inceptionV3_model)
+    inceptionV3 = InceptionV3("inceptionV3", madel_path)
 
 def Inference(input_image_path, output_image_path):
     # Read and preprocess the image.
     image = Image.open(input_image_path)
     image = preprocess_PIL_image(image).numpy()
     image = np.transpose(image, (0, 2, 3, 1))
-     
+
     # Burst the HTP.
     PerfProfile.SetPerfProfileGlobal(PerfProfile.BURST)
 
@@ -86,7 +124,7 @@ def Release():
 
 Init()
 
-Inference("input.jpg", "output.jpg")
+Inference(execution_ws + "\\input.jpg", "output.jpg")
 
 Release()
 

samples/python/real_esrgan_general_x4v3/README.md

@@ -4,7 +4,7 @@
 This is sample code for using AppBuilder to load real_esrgan_general_x4v3 QNN model to HTP and execute inference to upscale an image with minimal loss in quality. 
 
 ## Setup AppBuilder environment and prepare QNN SDK libraries by referring to the links below: 
-https://github.com/quic/ai-engine-direct-helper/blob/main/README.md
+https://github.com/quic/ai-engine-direct-helper/blob/main/README.md <br>
 https://github.com/quic/ai-engine-direct-helper/blob/main/Docs/User_Guide.md
 
 Copy the QNN libraries from QNN SDK to below path:

samples/python/real_esrgan_general_x4v3/real_esrgan_general_x4v3.py

@@ -35,6 +35,8 @@
 model_dir = execution_ws + "\\models"
 madel_path = model_dir + "\\" + MODEL_NAME + ".bin"
 
+####################################################################
+
 image_buffer = None
 realesrgan = None
 

samples/python/real_esrgan_x4plus/README.md

@@ -4,7 +4,7 @@
 This is sample code for using AppBuilder to load real_esrgan_x4plus QNN model to HTP and execute inference to generate image. 
 
 ## Setup AppBuilder environment and prepare QNN SDK libraries by referring to the links below: 
-https://github.com/quic/ai-engine-direct-helper/blob/main/README.md
+https://github.com/quic/ai-engine-direct-helper/blob/main/README.md <br>
 https://github.com/quic/ai-engine-direct-helper/blob/main/Docs/User_Guide.md
 
 Copy the QNN libraries from QNN SDK to below path:

samples/python/real_esrgan_x4plus/real_esrgan_x4plus.py

@@ -35,6 +35,8 @@
 model_dir = execution_ws + "\\models"
 madel_path = model_dir + "\\" + MODEL_NAME + ".bin"
 
+####################################################################
+
 image_buffer = None
 realesrgan = None
 

samples/python/riffusion/README.md

@@ -4,7 +4,7 @@
 This is sample code for using AppBuilder to load Riffusion QNN models to HTP and execute inference. 
 
 ## Setup AppBuilder environment and prepare QNN SDK libraries by referring to the links below: 
-https://github.com/quic/ai-engine-direct-helper/blob/main/README.md
+https://github.com/quic/ai-engine-direct-helper/blob/main/README.md <br>
 https://github.com/quic/ai-engine-direct-helper/blob/main/Docs/User_Guide.md
 
 Copy the QNN libraries from QNN SDK to below path:

samples/python/setup.py

@@ -8,9 +8,13 @@
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--qnn-sdk-version", default="2.24", type=str)
+    parser.add_argument("--lib-version", default="arm64x-windows-msvc", type=str)
+    parser.add_argument("--dsp-arch", default=73, type=int)
     args = parser.parse_args()
 
     qnn_sdk_version = args.qnn_sdk_version
+    lib_version = args.lib_version
+    dsp_arch = args.dsp_arch
 
     start_number = 140
     print()
@@ -23,8 +27,8 @@
     try:
         install.install_tools()
         install.install_qai_sdk(qnn_sdk_version)
-        install.install_qai_appbuilder(qnn_sdk_version)
-        install.setup_qai_env(qnn_sdk_version)
+        install.install_qai_appbuilder(qnn_sdk_version, lib_version)
+        install.setup_qai_env(qnn_sdk_version, lib_version, dsp_arch)
 
         print()
         print(start_number * "*")

samples/python/stable_diffusion_v1_5/README.md

@@ -4,7 +4,7 @@
 This is sample code for using AppBuilder to load Stable Diffusion 1.5 QNN models to HTP and execute inference to generate image. 
 
 ## Setup AppBuilder environment and prepare QNN SDK libraries by referring to the links below: 
-https://github.com/quic/ai-engine-direct-helper/blob/main/README.md
+https://github.com/quic/ai-engine-direct-helper/blob/main/README.md <br>
 https://github.com/quic/ai-engine-direct-helper/blob/main/Docs/User_Guide.md
 
 Copy the QNN libraries from QNN SDK to below path:

samples/python/stable_diffusion_v2_1/README.md

@@ -4,7 +4,7 @@
 This is sample code for using AppBuilder to load Stable Diffusion 2.1 QNN models to HTP and execute inference to generate image. 
 
 ## Setup AppBuilder environment and prepare QNN SDK libraries by referring to the links below: 
-https://github.com/quic/ai-engine-direct-helper/blob/main/README.md
+https://github.com/quic/ai-engine-direct-helper/blob/main/README.md <br>
 https://github.com/quic/ai-engine-direct-helper/blob/main/Docs/User_Guide.md
 
 Copy the QNN libraries from QNN SDK to below path: