docs/en/INFERENCE.md

Inference Guide

Scope

Use this guide only for PHPWind deployments you own or are explicitly authorized to test. The examples perform local image inference only; they do not automate account login flows or bypass access controls.

Specification

Item	Value
Input name	input
Input shape	[batch, 3, 64, 160]
Input type	float32, range [0,1] (RGB)
Output name	logits
Output shape	[batch, 4, 10]
Decode	argmax per position → digit
ONNX opset	18

Preprocessing is only three steps: RGB → resize to 160x64 (bilinear) → divide by 255. No grayscale, no denoising, no mean/std normalization.

Python

pip install onnxruntime pillow numpy

import numpy as np
import onnxruntime as ort
from PIL import Image

_SESSION = None

def get_session(model_path="model.onnx"):
    global _SESSION
    if _SESSION is None:
        _SESSION = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
    return _SESSION

def solve_captcha(img_bytes_or_path, model_path="model.onnx"):
    sess = get_session(model_path)
    im = Image.open(img_bytes_or_path).convert("RGB").resize((160, 64), Image.BILINEAR)
    x = np.asarray(im, dtype=np.float32) / 255.0
    x = x.transpose(2, 0, 1)[None]          # (1,3,64,160)
    logits = sess.run(None, {"input": x})[0] # (1,4,10)
    return "".join(str(int(logits[0, p].argmax())) for p in range(4))

code = solve_captcha("captcha.png")   # "4821"

Go (onnxruntime_go)

Deps: github.com/yalue/onnxruntime_go + the onnxruntime shared library (.dylib/.so).

package main

import (
    "bytes"
    "image"
    _ "image/png"

    "golang.org/x/image/draw"
    ort "github.com/yalue/onnxruntime_go"
)

const (
    inW, inH = 160, 64
    nDigits  = 4
)

type Solver struct {
    sess *ort.Session[float32]
    in   *ort.Tensor[float32]
    out  *ort.Tensor[float32]
}

func NewSolver(modelPath, libPath string) (*Solver, error) {
    if libPath != "" { ort.SetSharedLibraryPath(libPath) }
    if err := ort.InitializeEnvironment(); err != nil { return nil, err }
    in, err := ort.NewEmptyTensor[float32](ort.NewShape(1, 3, inH, inW))
    if err != nil { return nil, err }
    out, err := ort.NewEmptyTensor[float32](ort.NewShape(1, nDigits, 10))
    if err != nil { in.Destroy(); return nil, err }
    sess, err := ort.NewSession[float32](modelPath,
        []string{"input"}, []string{"logits"},
        []*ort.Tensor[float32]{in}, []*ort.Tensor[float32]{out})
    if err != nil { in.Destroy(); out.Destroy(); return nil, err }
    return &Solver{sess, in, out}, nil
}

func (s *Solver) Solve(png []byte) (string, error) {
    src, _, err := image.Decode(bytes.NewReader(png))
    if err != nil { return "", err }
    dst := image.NewRGBA(image.Rect(0, 0, inW, inH))
    draw.ApproxBiLinear.Scale(dst, dst.Bounds(), src, src.Bounds(), draw.Over, nil)

    data := s.in.GetData()
    idx := 0
    // NCHW layout: all R, then all G, then all B
    for y := 0; y < inH; y++ { for x := 0; x < inW; x++ { r,_,_,_ := dst.At(x,y).RGBA(); data[idx]=float32(r>>8)/255; idx++ } }
    for y := 0; y < inH; y++ { for x := 0; x < inW; x++ { _,g,_,_ := dst.At(x,y).RGBA(); data[idx]=float32(g>>8)/255; idx++ } }
    for y := 0; y < inH; y++ { for x := 0; x < inW; x++ { _,_,b,_ := dst.At(x,y).RGBA(); data[idx]=float32(b>>8)/255; idx++ } }

    if err := s.sess.Run(); err != nil { return "", err }
    got := s.out.GetData()
    code := make([]byte, nDigits)
    for p := 0; p < nDigits; p++ {
        best := 0
        for c := 1; c < 10; c++ { if got[p*10+c] > got[p*10+best] { best = c } }
        code[p] = '0' + byte(best)
    }
    return string(code), nil
}

Note: onnxruntime_go.NewSession reads the model from a file. If you embed it with go:embed, use NewSessionWithONNXData with the bytes instead.

Performance

• Single inference: ~5-15ms (CPU, M1/modern x86)
• Full flow (load + preprocess + infer): ~10-30ms
• No GPU needed, memory < 50MB