传数据#####
[木子加贝]:
from rknnlite.api import RKNNLite
import cv2
import numpy as np
# 后处理函数
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def process_yolov5_output(outputs, img_size=(640, 640), conf_threshold=0.4, iou_threshold=0.5):
predictions = outputs[0][0]
boxes = predictions[:, :4]
confidences = sigmoid(predictions[:, 4:5])
class_scores = sigmoid(predictions[:, 5:])
scores = confidences * class_scores
class_ids = np.argmax(scores, axis=1)
max_scores = np.max(scores, axis=1)
mask = max_scores > conf_threshold
boxes = boxes[mask]
class_ids = class_ids[mask]
scores = max_scores[mask]
boxes[:, 0] = boxes[:, 0] - boxes[:, 2] / 2
boxes[:, 1] = boxes[:, 1] - boxes[:, 3] / 2
boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
indices = cv2.dnn.NMSBoxes(
boxes=boxes.tolist(),
scores=scores.tolist(),
score_threshold=conf_threshold,
nms_threshold=iou_threshold
传数据#####
[木子加贝]:
from rknnlite.api import RKNNLite
import cv2
import numpy as np
# 1. 初始化 RKNNLite 对象
rknn = RKNNLite()
# 2. 加载 RKNN 模型
MODEL_PATH = 'yolov5.rknn' # 替换为你的模型路径
ret = rknn.load_rknn(MODEL_PATH)
if ret != 0:
print("加载 RKNN 模型失败！")
exit(1)
# 3. 初始化 RKNN Lite 运行环境
ret = rknn.init_runtime()
if ret != 0:
print("初始化 RKNN 运行环境失败！")
exit(1)
# 4. 加载并预处理输入图像
IMG_PATH = 'input.jpg' # 替换为你的输入图片路径
img = cv2.imread(IMG_PATH)
img = cv2.resize(img, (640, 640)) # 根据模型输入大小调整
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img / 255.0 # 归一化
img = np.expand_dims(img, axis=0).astype(np.float32)
# 5. 推理
outputs = rknn.inference(inputs=[img])
# 6. 处理推理结果
# 根据 YOLOv5 的输出格式解析结果
# 通常需要后处理（如 NMS）来获取最终的检测框
print("推理结果：", outputs)
# 7. 释放 RKNN 资源
rknn.release()
Pytorch深度学习环境配置教程
[木子加贝]:
# 安装依赖
sudo apt update
sudo apt install python3 python3-pip
pip3 install fastapi uvicorn transformers torch sentencepiece
# 创建服务脚本 (model_api.py)
```doubaocanvas identifier="deepseek-web-api" type="general_code" title="DeepSeek模型API服务">
```python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
app = FastAPI()
# 模型加载
model_name = "deepseek-ai/deepseek-coder-1.3b-base" # 根据实际路径调整
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16, # 使用半精度减少内存占用
device_map="auto", # 自动管理设备分配
)
# 定义请求模型
class GenerationRequest(BaseModel):
prompt: str
max_length: int = 100
temperature: float = 0.7
# 生成API
@app.post("/generate")
async def generate_text(request: GenerationRequest):
try:
Pytorch深度学习环境配置教程
[木子加贝]:
# 安装依赖
sudo apt update
sudo apt install python3 python3-pip
pip3 install fastapi uvicorn transformers torch sentencepiece
# 创建服务脚本 (model_api.py)
```doubaocanvas identifier="deepseek-web-api" type="general_code" title="DeepSeek模型API服务">
```python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
app = FastAPI()
# 模型加载
model_name = "deepseek-ai/deepseek-coder-1.3b-base" # 根据实际路径调整
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16, # 使用半精度减少内存占用
device_map="auto", # 自动管理设备分配
)
# 定义请求模型
class GenerationRequest(BaseModel):
prompt: str
max_length: int = 100
temperature: float = 0.7
# 生成API
@app.post("/generate")
async def generate_text(request: GenerationRequest):
try:
一、新手学习爬虫第一课 对网站发起请求（基于python语言）
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