add python demos

examples/resnet/py/resnet.py

@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+"""
+Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import os
+import argparse
+import glob
+import cv2
+import numpy as np
+from pathlib import Path
+from amlnnlite.api import AMLNNLite
+
+MEAN = np.array([123.675, 116.28, 103.53], dtype=np.float32)
+STD  = np.array([58.395, 58.395, 58.395], dtype=np.float32)
+
+def preprocess(img_path):
+    img = cv2.imread(img_path)
+    if img is None:
+        return None
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_LINEAR)
+    img = img.astype(np.float32)
+
+    img = (img - MEAN) / STD
+    img = np.expand_dims(img, axis=0)  
+    return img
+
+def postprocess_topk(logits, labels, k=5):
+    logits = logits.squeeze()
+    idx = np.argsort(logits)[::-1][:k]
+
+    print(f"\n    Top-{k} Results:")
+    for i, c in enumerate(idx):
+        name = labels[c] if c < len(labels) else f"Unknown({c})"
+        score = logits[c]
+        print(f"      {i+1}. {name:20s}  score={score:.6f}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Classification AMLNNLite Demo")
+
+    parser.add_argument('--board-work-path', default='/data/nn', help='Work path on board')
+    parser.add_argument('--model-path', required=True, help='Path to .adla or .tflite model')
+    parser.add_argument('--image-dir', required=True, help='Directory containing test images')
+    parser.add_argument('--labels', required=True, help='Path to synset_words.txt or labels.txt')
+    parser.add_argument('--run-cycles', type=int, default=1, help='Number of inference cycles')
+    parser.add_argument('--loglevel', default='WARNING', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'])
+
+    args = parser.parse_args()
+
+    amlnn = AMLNNLite()
+    amlnn.config(
+        board_work_path=args.board_work_path,
+        model_path=args.model_path,
+        run_cycles=args.run_cycles,
+        loglevel=args.loglevel
+    )
+    amlnn.init()
+
+    if not os.path.exists(args.labels):
+        print(f"Error: Label file not found: {args.labels}")
+        amlnn.uninit(); return
+        
+    with open(args.labels, "r") as f:
+        labels = [line.strip() for line in f.readlines()]
+
+    image_files = []
+    for ext in ['*.jpg', '*.jpeg', '*.png', '*.bmp']:
+        image_files.extend(glob.glob(os.path.join(args.image_dir, ext)))
+        image_files.extend(glob.glob(os.path.join(args.image_dir, ext.upper())))
+    image_files.sort()
+
+    if not image_files:
+        print(f"No image files found in: {args.image_dir}")
+        amlnn.uninit(); return
+
+    total_images = len(image_files)
+
+    for idx, img_path in enumerate(image_files, start=1):
+        print("=" * 60)
+        print(f"Processing image {idx}/{total_images}: {os.path.basename(img_path)}")
+        print("=" * 60)
+
+        inp = preprocess(img_path)
+        if inp is None:
+            print(f"    Skip: Cannot read {img_path}")
+            continue
+
+        for _ in range(args.run_cycles):
+            outputs = amlnn.inference(
+                inp,
+                inputs_data_format='NHWC',
+                outputs_data_format='NHWC'
+            )
+
+        postprocess_topk(outputs[0], labels, k=5)
+
+    amlnn.visualize()
+    amlnn.uninit()
+
+if __name__ == "__main__":
+    main()