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  • 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



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