YOLOv5车牌识别实战教程(六)性能优化与部署

import torch
import torch.nn as nn
import torch.optim as optim
 
def distillation(teacher_model, student_model, data_loader, epochs=10, temperature=2):
    criterion = nn.KLDivLoss()
    optimizer = optim.Adam(student_model.parameters(), lr=0.001)
 
    for epoch in range(epochs):
        for inputs, labels in data_loader:
            teacher_outputs = teacher_model(inputs).detach()
            teacher_outputs = nn.Softmax(dim=1)(teacher_outputs / temperature)
 
            student_outputs = student_model(inputs)
            student_outputs = nn.LogSoftmax(dim=1)(student_outputs / temperature)
 
            loss = criterion(student_outputs, teacher_outputs)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
 
    return student_model

import torch
 
torch_model = YOLOv5Model()
torch_model.eval()
onnx_model_path = "yolov5_plate_detection.onnx"
dummy_input = torch.randn(1, 3, 640, 640)
torch.onnx.export(torch_model, dummy_input, onnx_model_path, input_names=['input'], output_names=['output'])

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
 
def build_engine_onnx(onnx_file_path):
    TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(TRT_LOGGER)
    network = builder.create_network(common.EXPLICIT_BATCH)
    parser = trt.OnnxParser(network, TRT_LOGGER)
 
    with open(onnx_file_path, 'rb') as model:
        parser.parse(model.read())
 
    config = builder.create_builder_config()
    config.max_workspace_size = 1 << 30
    engine = builder.build_engine(network, config)
    return engine
 
def inference(engine, input_data):
    context = engine.create_execution_context()
    input_shape = engine.get_binding_shape(0)
    output_shape = engine.get_binding_shape(1)
 
    input_data = input_data.numpy().astype(np.float32)
    input_data = input_data.ravel()
 
    d_input = cuda.mem_alloc(1 * input_data.nbytes)
    d_output = cuda.mem_alloc(1 * np.prod(output_shape) * 4)
 
    cuda.memcpy_htod(d_input, input_data)
    context.execute(1, [int(d_input), int(d_output)])
 
    output_data = np.empty(output_shape, dtype=np.float32)
    cuda.memcpy_dtoh(output_data, d_output)
 
    return output_data
 
onnx_file_path = "yolov5_plate_detection.onnx"
engine = build_engine_onnx(onnx_file_path)
 
input_data = torch.randn(1, 3, 640, 640)
output_data = inference(engine, input_data)

from flask import Flask, request, jsonify
import torch
import cv2
import base64
import io
import numpy as np
 
app = Flask(__name__)
model = YOLOv5Model()
 
@app.route('/predict', methods=['POST'])
def predict():
    img_data = request.files['image'].read()
    img_array = np.frombuffer(img_data, np.uint8)
    img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
 
    with torch.no_grad():
        detections = model(img)
 
    results = process_detections(detections)
    return jsonify(results)
 
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

import tkinter as tk
from tkinter import filedialog
from PIL import Image, ImageTk
import cv2
import torch
 
model = YOLOv5Model()
 
def load_image():
    file_path = filedialog.askopenfilename()
    img = cv2.imread(file_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img
 
def recognize_plate():
    img = load_image()
    with torch.no_grad():
        detections = model(img)
 
    results = process_detections(detections)
    display_results(img, results)
 
def display_results(img, results):
    img = Image.fromarray(img)
    img = ImageTk.PhotoImage(img)
 
    result_label.config(image=img)
    result_label.image = img
    result_text.delete(1.0, tk.END)
    result_text.insert(tk.END, results)
 
root = tk.Tk()
 
result_label = tk.Label(root)
result_label.pack()
 
result_text = tk.Text(root)
result_text.pack()
 
button = tk.Button(root, text='Recognize Plate', command=recognize_plate)
button.pack()
 
root.mainloop()

from albumentations import (
    Compose, Rotate, RandomScale, HorizontalFlip, RandomCrop,
    HueSaturationValue, GaussianNoise
)
 
transform = Compose([
    Rotate(limit=10, p=0.5),
    RandomScale(scale_limit=0.2, p=0.5),
    HorizontalFlip(p=0.5),
    RandomCrop(height=640, width=640, p=0.5),
    HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
    GaussianNoise(var_limit=(10.0, 50.0), p=0.5)
])
 
augmented_image = transform(image=image)['image']