如何使用tf.free_from_fb()释放导入内存
-
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
-
使用OpenMV4 H7 Plus 不会有问题,你用的是OpenMV 4?