111 lines
3.8 KiB
Python
111 lines
3.8 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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import torch
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from yolov5_preprocess import Yolov5_preprocess
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import kneron_preprocessing
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# 設定 ONNX 模型儲存路徑
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onnx_dir = 'runs/train/exp29/weights/'
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onnx_path = os.path.join(onnx_dir, 'best_simplified.onnx')
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# 確保目標資料夾存在
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os.makedirs(onnx_dir, exist_ok=True)
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# 加載並優化 ONNX 模型
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m = onnx.load(onnx_path)
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m = ktc.onnx_optimizer.onnx2onnx_flow(m)
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opt_onnx_path = os.path.join(onnx_dir, 'latest.opt.onnx')
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onnx.save(m, opt_onnx_path)
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km = ktc.ModelConfig(20008, "0001", "630", onnx_model=m)
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eval_result = km.evaluate()
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print("\nNpu performance evaluation result:\n" + str(eval_result))
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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imgsz_h, imgsz_w = 640, 640
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data_path = "data4"
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files_found = [f for _, _, files in os.walk(data_path) 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"\u274c Error: No images found in {data_path}! Please check your dataset.")
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print(f"\u2705 Found {len(files_found)} images in {data_path}")
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# 獲取 ONNX 模型的輸入名稱
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input_name = m.graph.input[0].name # 確保 key 與 ONNX input name 一致
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# 存儲預處理後的圖片數據
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img_list = []
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# 遍歷 data50 並進行預處理
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for root, _, files in os.walk(data_path):
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for f in files:
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fullpath = os.path.join(root, f)
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# 只處理圖片文件
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if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
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print(f"\u26a0\ufe0f Skipping non-image file: {fullpath}")
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continue
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# 嘗試處理圖片
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try:
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img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
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img_data = img_data.cpu().numpy()
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print(f"\u2705 Processed: {fullpath}")
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img_list.append(img_data)
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except Exception as e:
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print(f"\u274c Failed to process {fullpath}: {e}")
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# 確保 img_list 不是空的
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if not img_list:
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raise ValueError("\u274c Error: No valid images were processed! Please check the image paths and formats.")
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# 執行 BIE 量化
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bie_model_path = km.analysis({input_name: img_list})
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# 確保 BIE 檔案儲存到指定目錄
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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) # 使用 shutil.move 來處理跨磁碟移動
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# 確認 BIE 模型是否生成
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if not os.path.exists(bie_save_path):
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raise RuntimeError(f"\u274c Error: BIE model was not generated! Please check your quantization process.")
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print("\n\u2705 Fixed-point analysis done! BIE model saved to:", bie_save_path)
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# 確保 `km` 已經初始化，並且 `.bie` 模型已生成
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nef_model_path = ktc.compile([km])
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# 確保 nef_model_path 不是 None 或空值
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if not nef_model_path:
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raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
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# 確保 NEF 目標資料夾存在
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os.makedirs(onnx_dir, exist_ok=True)
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# 確保 nef_model_path 不是 None 或空值
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if not nef_model_path:
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raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
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# 確保 .nef 檔案存在
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if not os.path.exists(nef_model_path):
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raise RuntimeError(f"❌ Error: NEF model was not generated at {nef_model_path}! Please check your compilation process.")
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# 確保 NEF 檔案儲存到指定目錄
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nef_save_path = os.path.join(onnx_dir, os.path.basename(nef_model_path))
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if os.path.exists(nef_model_path):
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shutil.copy(nef_model_path, nef_save_path)
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else:
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raise RuntimeError(f"❌ Error: NEF model was expected at {nef_model_path}, but it does not exist!") # 同樣使用 shutil.move
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if not os.path.exists(nef_save_path):
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raise RuntimeError(f"\u274c Error: NEF model was not generated! Please check your compilation process.")
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print("\n\u2705 Compile done! NEF file saved to:", nef_save_path)
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