H7 Plus的额RAM不是32M吗？感觉很容易内存就没了。而且我经过手动内存释放后，还是会有内存不足的提示。

import sensor
import network
import socket
import gc
from pyb import UART
from pyb import LED

led = LED(1)
led_p = LED(2)
led.on()
led_p.off()

uart = UART(3, 9600)

type_ = "3"
type_data =("\r\n" + type_).encode('utf-8')

# AP info
SSID = ""  # Network SSID
KEY = ""  # Network key

PORT = 9999
HOST = ""

# 构建 HTTP 请求头和主体
boundary = '----WebKitFormBoundary7MA4YWxkTrZu0gW'
content_type = 'image/jpg'
# 构建请求体
body_start_type = (
    "\r\n"
    "--" + boundary + "\r\n"
    "Content-Disposition: form-data; name=\"type\"\r\n"
).encode('utf-8')
# 构建请求体尾
body_end = ("\r\n--" + boundary + "--\r\n").encode('utf-8')

# 初始化感光元件
sensor.reset()
sensor.set_pixformat(sensor.RGB565) # Modify as you like.
sensor.set_framesize(sensor.SVGA) # Modify as you like.

# 初始化wlan模块并连接到网络
wlan = network.WLAN(network.STA_IF)
wlan.active(True)
wlan.connect(SSID, KEY)

while not wlan.isconnected():
    print('Trying to connect to "{:s}"...'.format(SSID))
    time.sleep_ms(1000)

led.off()
led_p.on()

print("WiFi Connected ", wlan.ifconfig())

# 通过DNS获取addr信息
addr = socket.getaddrinfo(HOST, PORT)[0][4]
print(addr)
del addr

while True:
    read_data = uart.read(10)
    if read_data != None:
        if read_data and read_data != b'\x00' * 10:  # 检查数据是否全是0

            # 解码字节串为字符串
            string_data = read_data.decode('utf-8')
            del read_data

            # 添加文件扩展名
            filename = string_data + ".jpg"
            del string_data

            led_p.off()

            # 拍照
            frame = sensor.snapshot()
            frame.save(filename)
            del frame

            f = open(filename, 'rb')
            file_data = f.read()
            f.close()
            del f

            # 构建请求体
            body_start_data = (
                "--" + boundary + "\r\n"
                "Content-Disposition: form-data; name=\"image\"; filename=\"" + filename + "\"\r\n"
                "Content-Type: " + content_type + "\r\n\r\n"
            ).encode('utf-8')
            del filename

            # 完整的请求体
            body = (body_start_data + file_data + body_start_type + type_data + body_end)
            del body_start_data
            del file_data

            # 构建请求头
            headers = (
                "POST /images/upload HTTP/1.1\r\n"
                "Host: " + HOST + ":" + str(PORT) + "\r\n"
                "Content-Length: " + str(len(body)) + "\r\n"
                "Content-Type: multipart/form-data; boundary=" + boundary + "\r\n"
                "Connection: keep-alive\r\n\r\n"
            ).encode('utf-8')

            # 完整的请求
            request_data = headers + body
            del headers
            del body

            # 创建 socket 连接
            sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
            sock.connect((HOST, PORT))

            # 发送 HTTP 请求
            sock.sendall(request_data)
            del request_data

            # 接收服务器响应
            data = sock.recv(4096)

            sock.close()
            del sock

            led_p.on()

            gc.collect()

请在这里粘贴代码

sensor.reset()                         # Reset and initialize the sensor.
    sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
    sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
    sensor.set_windowing((240, 240))       # Set 240x240 window.
    sensor.skip_frames(time=2000)          # Let the camera adjust.

    net = None
    labels = None
    min_confidence = 0.5

    try:
        # load the model, alloc the model file on the heap if we have at least 64K free after loading
        net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
    except Exception as e:
        raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

    try:
        labels = [line.rstrip('\n') for line in open("labels.txt")]
    except Exception as e:
        raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

    colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
        (255,   0,   0),
        (  0, 255,   0),
        (255, 255,   0),
        (  0,   0, 255),
        (255,   0, 255),
        (  0, 255, 255),
        (255, 255, 255),
    ]

    clock = time.clock()

    clock.tick()

    Mask_Flag = False
    Count = 0
    Check_Num = 40

    for i in range(Check_Num):

        img = sensor.snapshot()

        # detect() returns all objects found in the image (splitted out per class already)
        # we skip class index 0, as that is the background, and then draw circles of the center
        # of our objects

        for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
            if (i == 0):
                continue # background class

            if (len(detection_list) == 0):
                continue # no detections for this class?

            print("********** %s **********" % labels[i])

            for d in detection_list:
                [x, y, w, h] = d.rect()
                center_x = math.floor(x + (w / 2))
                center_y = math.floor(y + (h / 2))
                print('x %d\ty %d' % (center_x, center_y))
                img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)

            if("mask" == labels[i]):
                Count = Count + 1
                if(Count >= 20):
                    Mask_Flag = True
                    break

    tf.free_from_fb()
    print(clock.fps(), "fps", end="\n\n")

使用net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))加载后想释放内存，看了函数库tf.free_from_fb()可以释放内存，但是再载入模型报错内存不足。手册上tf.free_from_fb()没有输入参数，我想知道如何使用这个函数