在Google Colab上优化张量流模型的推理速度

Question

我正在使用预先训练的 TensorFlow 模型在具有GPU的Google Colab 中进行对象检测。但是，推理时间明显高于Tensorflow模型动物园中报告的速度。

任何人都可以告诉我这样做的原因，以及如何使其运行得更快吗？

此外，我正在尝试处理非常大的图像。因此，我正在使用滑动窗口来平铺图像并在每个平铺上进行检测。有更好的方法吗？

非常感谢！

import numpy as np
import cv2
import time

image_path = "/content/drive/My Drive/datasets/20190206142141.jpg"

img = np.array(Image.open(image_path))
img = cv2.resize(img, (3000,2000))

(winW, winH) = (500,500)

i = 0
imgs=[]
dics=[]
for (x, y, window) in sliding_window(img, stepSize=500, windowSize=(winW, winH)):
  if window.shape[0] != winH or window.shape[1] != winW:
    continue
  print(x, x+winW, y, y+winH)
  crop_img = img[y:y+winH, x:x+winW]
  start_time = time.time()
  img_i, dic_i = detect(detection_model, crop_img)
  print("--- %s seconds ---" % (time.time() - start_time))

  imgs.append(img_i)
  dics.append(dic_i)
  display(Image.fromarray(imgs[i]))

  i = i+1

half1 = cv2.hconcat([imgs[0], imgs[1], imgs[2], imgs[3], imgs[4], imgs[5]])
half2 = cv2.hconcat([imgs[6], imgs[7], imgs[8], imgs[9], imgs[10], imgs[11]])
half3 = cv2.hconcat([imgs[12], imgs[13], imgs[14], imgs[15], imgs[16], imgs[17]])
half4 = cv2.hconcat([imgs[18], imgs[19], imgs[20], imgs[21], imgs[22], imgs[23]])
full_img = cv2.vconcat([half1, half2, half3, half4])
full_img = cv2.resize(full_img, (1500,1000))

display(Image.fromarray(full_img))


# supporting functions
def sliding_window(image, stepSize, windowSize):
    # slide a window across the image
    for y in range(0, image.shape[0], stepSize):
        for x in range(0, image.shape[1], stepSize):
            # yield the current window
            yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])

def detect(model, image_np):
  # the array based representation of the image will be used later in order to prepare the
  # result image with boxes and labels on it.
  # Actual detection.

  start_time = time.time()
  output_dict = run_inference_for_single_image(model, image_np)
  print("--- %s seconds (detection) ---" % (time.time() - start_time))

  # Visualization of the results of a detection.
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,

      instance_masks=output_dict.get('detection_masks_reframed', None),

      use_normalized_coordinates=True,
      line_thickness=2)

  print(output_dict['detection_boxes'])

  # display(Image.fromarray(image_np))
  return image_np, output_dict