AI训练后运行时提示无法加载模型？

[kyhe]

我自己在edge implus上训练的模型，我用的MV4,

U盘里我也放文件了啊

帮忙看看谢谢，
一开始训练的模型需加载ml，我看视频教程是tf，后来我就改成了tf

Edge Impulse - OpenMV FOMO Object Detection Example

This work is licensed under the MIT license.

Copyright (c) 2013-2024 OpenMV LLC. All rights reserved.

https://github.com/openmv/openmv/blob/master/LICENSE

import sensor, image, time, os, tf,math, uos, gc
from ulab import numpy as np

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.Model("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),
]

threshold_list = [(math.ceil(min_confidence * 255), 255)]

def fomo_post_process(model, inputs, outputs):
ob, oh, ow, oc = model.output_shape[0]

x_scale = inputs[0].roi[2] / ow
y_scale = inputs[0].roi[3] / oh

scale = min(x_scale, y_scale)

x_offset = ((inputs[0].roi[2] - (ow * scale)) / 2) + inputs[0].roi[0]
y_offset = ((inputs[0].roi[3] - (ow * scale)) / 2) + inputs[0].roi[1]

l = [[] for i in range(oc)]

for i in range(oc):
    img = image.Image(outputs[0][0, :, :, i] * 255)
    blobs = img.find_blobs(
        threshold_list, x_stride=1, y_stride=1, area_threshold=1, pixels_threshold=1
    )
    for b in blobs:
        rect = b.rect()
        x, y, w, h = rect
        score = (
            img.get_statistics(thresholds=threshold_list, roi=rect).l_mean() / 255.0
        )
        x = int((x * scale) + x_offset)
        y = int((y * scale) + y_offset)
        w = int(w * scale)
        h = int(h * scale)
        l[i].append((x, y, w, h, score))
return l

clock = time.clock()
while(True):
clock.tick()

img = sensor.snapshot()

for i, detection_list in enumerate(net.predict([img], callback=fomo_post_process)):
    if i == 0: continue  # background class
    if len(detection_list) == 0: continue  # no detections for this class?

    print("********** %s **********" % labels[i])
    for x, y, w, h, score in detection_list:
        center_x = math.floor(x + (w / 2))
        center_y = math.floor(y + (h / 2))
        print(f"x {center_x}\ty {center_y}\tscore {score}")
        img.draw_circle((center_x, center_y, 12), color=colors[i])

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

[kidswong999]

代码不对，升级固件，然后用生成的代码。