OpenCV 3 Computer Vision with Python Cookbook

How to do it...

You need to perform the following steps:

1. Import the modules:

import cv2import numpy as np

2. Import the Caffe model:

model = cv2.dnn.readNetFromCaffe('../data/bvlc_googlenet.prototxt',                                 '../data/bvlc_googlenet.caffemodel')

3. Compute the number of FLOPs performed in the inference stage:

print('gflops:', model.getFLOPS((1,3,224,224))*1e-9)

4. Report the amount of memory consumed for storing weights and intermediate tensors:

w,b = model.getMemoryConsumption((1,3,224,224))print('weights (mb):', w*1e-6, ', blobs (mb):', b*1e-6)

5. Perform a forward pass for a mock input:

blob = cv2.dnn.blobFromImage(np.zeros((224,224,3), np.uint8), 1, (224,224))model.setInput(blob)model.forward()

6. Report the total time:

total,timings = model.getPerfProfile() ...