charlie880624
7716a0060f
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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
import ktc
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import numpy as np
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import os
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import onnx
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import shutil
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from PIL import Image
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# === 1. 設定路徑與參數 ===
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onnx_dir = 'work_dirs/meconfig8/'
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onnx_path = os.path.join(onnx_dir, 'latest.onnx')
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optimized_path = os.path.join(onnx_dir, 'latest_optimized.onnx')
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data_path = 'data724362'
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imgsz_w, imgsz_h = 724, 362 # STDC 預設解析度
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# === 2. 建立輸出資料夾 ===
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os.makedirs(onnx_dir, exist_ok=True)
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# === 3. 優化 ONNX 模型（使用 onnx2onnx_flow）===
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print("⚙️ 使用 onnx2onnx_flow 優化 ONNX...")
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model = onnx.load(onnx_path)
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model = ktc.onnx_optimizer.onnx2onnx_flow(model)
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onnx.save(model, optimized_path)
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# === 4. 驗證輸入 Shape 是否正確 ===
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print("📏 驗證 ONNX Input Shape...")
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input_tensor = model.graph.input[0]
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input_name = input_tensor.name
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input_shape = [d.dim_value for d in input_tensor.type.tensor_type.shape.dim]
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expected_shape = [1, 3, imgsz_h, imgsz_w]
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print(f"📌 input_name: {input_name}")
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print(f"📌 input_shape: {input_shape}")
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if input_shape != expected_shape:
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raise ValueError(f"❌ Shape mismatch: {input_shape} ≠ {expected_shape}")
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# === 5. 初始化模型編譯器 (for KL630) ===
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print("📐 配置模型 for KL630...")
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km = ktc.ModelConfig(40000, "0001", "730", onnx_model=model)
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# （可選）效能分析
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eval_result = km.evaluate()
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print("\n📊 NPU 效能分析:\n" + str(eval_result))
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# === 6. 圖片預處理 ===
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print("🖼️ 處理輸入圖片...")
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img_list = []
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files_found = [f for _, _, files in os.walk(data_path)
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for f in files if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp"))]
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if not files_found:
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raise FileNotFoundError(f"❌ 找不到圖片於 {data_path}")
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for root, _, files in os.walk(data_path):
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for f in files:
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if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
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continue
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fullpath = os.path.join(root, f)
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try:
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img = Image.open(fullpath).convert("RGB")
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img = Image.fromarray(np.array(img)[..., ::-1]) # RGB ➝ BGR
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img_np = np.array(img.resize((imgsz_w, imgsz_h), Image.BILINEAR)).astype(np.float32)
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img_np = img_np / 256.0 - 0.5
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img_np = np.transpose(img_np, (2, 0, 1)) # HWC ➝ CHW
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img_np = np.expand_dims(img_np, axis=0) # CHW ➝ NCHW
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img_list.append(img_np)
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print(f"✅ 處理成功: {fullpath}")
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except Exception as e:
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print(f"❌ 處理失敗 {fullpath}: {e}")
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if not img_list:
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raise RuntimeError("❌ 沒有成功處理任何圖片！")
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# === 7. 執行 BIE 量化分析 ===
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print("📦 執行固定點分析 (BIE)...")
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bie_model_path = km.analysis({input_name: img_list})
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bie_save_path = os.path.join(onnx_dir, os.path.basename(bie_model_path))
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shutil.copy(bie_model_path, bie_save_path)
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if not os.path.exists(bie_save_path):
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raise RuntimeError("❌ 無法產生 BIE 模型")
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print("✅ BIE 模型儲存於:", bie_save_path)
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# === 8. 編譯 NEF 模型 ===
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print("⚙️ 編譯 NEF 模型 for KL630...")
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nef_model_path = ktc.compile([km])
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nef_save_path = os.path.join(onnx_dir, os.path.basename(nef_model_path))
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shutil.copy(nef_model_path, nef_save_path)
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if not os.path.exists(nef_save_path):
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raise RuntimeError("❌ 無法產生 NEF 模型")
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print("✅ NEF 編譯完成")
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print("📁 NEF 檔案儲存於:", nef_save_path)
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