cv_resnet18_human-detection Ascend NPU 部署指南

cv_resnet18_human-detection-ascend/
├── inference.py              # 推理测试脚本
├── log.txt                  # 测试日志
├── README.md                # 本文档
├── test_human.jpg           # 测试图片
├── result_visualization.jpg  # 检测结果可视化
├── precision_result.json    # 精度测试结果
├── inference_result.json    # 推理输出结果
└── fusion_result.json       # 图融合结果

docker exec -it test-modelagent bash

source /usr/local/Ascend/ascend-toolkit/set_env.sh

pip install torch torch_npu opencv-python numpy

cd /data/ysws/agentsp/5-19-1/cv_resnet18_human-detection-ascend/

# 运行人体检测
python3 inference.py

cd /data/ysws/agentsp/5-19-1/cv_resnet18_human-detection-ascend/

# 运行完整精度测试
python3 inference.py precision_test

import torch
import cv2
import numpy as np

MODEL_PATH = "/data/ysws/agentsp/5-19-1/cv_resnet18_human-detection/iic/cv_resnet18_human-detection/pytorch_model.pt"

# 加载模型
model = HumanDetectionModel(MODEL_PATH)
device = torch.device("npu:0")
model = model.to(device)
model.eval()

# 读取图片
img = cv2.imread("test_human.jpg")
img_tensor, scale, _, _ = preprocess(img)
img_tensor = torch.from_numpy(img_tensor).unsqueeze(0).to(device)

# 推理
with torch.no_grad():
    cls_score, bbox_pred = model(img_tensor)

# 解码检测结果
detections = decode_detections(cls_score, bbox_pred, scale, img.shape)
print(f"检测到 {len(detections)} 个人体")

import os

image_dir = "/path/to/images"
image_files = [f for f in os.listdir(image_dir) if f.endswith('.jpg')]

for img_file in image_files:
    img_path = os.path.join(image_dir, img_file)
    img = cv2.imread(img_path)

    # 预处理
    img_tensor, scale, _, _ = preprocess(img)
    img_tensor = torch.from_numpy(img_tensor).unsqueeze(0).to(device)

    # 推理
    with torch.no_grad():
        cls_score, bbox_pred = model(img_tensor)

    detections = decode_detections(cls_score, bbox_pred, scale, img.shape)

    # 保存可视化结果
    vis_path = os.path.join(OUTPUT_DIR, f"result_{img_file}")
    visualize(img, detections, vis_path)

# 图像预处理
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    to_rgb=True
)

# 测试图像增强
img_scale=(1333, 800)
size_divisor=32

# 检测阈值
score_thr=0.6
nms_threshold=0.7 (rpn), 0.5 (rcnn)