34 lines
964 B
Python
34 lines
964 B
Python
import os
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import numpy as np
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import torch
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from yolov5_preprocess import Yolov5_preprocess # 使用你的預處理
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import kneron_preprocessing
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# 設定裝置
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# 設定圖片大小（與訓練時一致）
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imgsz_h, imgsz_w = 640, 640
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# 量化數據集目錄（請確保這個資料夾存在）
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data_path = "/data50"
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img_list = []
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# 遍歷 voc_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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# 執行與訓練相同的預處理
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img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
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print(f"Processed: {fullpath}")
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img_list.append(img_data)
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# 轉為 NumPy 格式
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img_list = np.array(img_list)
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# 執行 BIE 量化分析
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bie_model_path = km.analysis({"input": img_list})
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print("\nFixed-point analysis done. Saved bie model to '" + str(bie_model_path) + "'")
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