Before I move on to object detection I thought I would try one more example of object classification using a more complex neural network based on the Multi-layer offload architecture.  The network used is a variant of the DoReFa-Net and uses the large ImageNet dataset http://www.image-net.org/  for training.  The DoReFa-Net https://arxiv.org/pdf/1606.06160 is a low bitwidth convolutional neural network that is trained with low bitwidth gradients optimized for implementation on hardware like FPGAs.

 

ImageNet Classifier:

The network topology is shown below.  The pink layers are executed in the Programmable Logic at reduced precision (1 bit for weights, 2 bit for activations) while the other layers are executed in python.

 

Initialize the network

  1. Import libraries
  2. Instantiate classifier
  3. Load labels and synsets of the 1000 ImageNet classes into dictionaries

 

Code for initialization:

import os, pickle, random
from datetime import datetime
from matplotlib import pyplot as plt
from PIL import Image
%matplotlib inline

import numpy as np
import cv2

import qnn
from qnn import Dorefanet
from qnn import utils

# Instantiate a classifier
classifier = Dorefanet()
classifier.init_accelerator()
net = classifier.load_network(json_layer="/usr/local/lib/python3.6/dist-packages/qnn/params/dorefanet-layers.json")

conv0_weights = np.load('/usr/local/lib/python3.6/dist-packages/qnn/params/dorefanet-conv0.npy', encoding="latin1").item()
fc_weights = np.load('/usr/local/lib/python3.6/dist-packages/qnn/params/dorefanet-fc-normalized.npy', encoding='latin1').item()

# Get ImageNet Classes information
with open("/home/xilinx/jupyter_notebooks/qnn/imagenet-classes.pkl", 'rb') as f:
    classes = pickle.load(f)
    names = dict((k, classes[k][1].split(',')[0]) for k in classes.keys())
    synsets = dict((classes[k][0], classes[k][1].split(',')[0]) for k in classes.keys())

 

Classify image

  1. Open image to be classified
  2. Execute the first convolutional layer in Python
  3. Compute HW Offload of the quantized layers
  4. Normalize using fully connected layers in python

 

Code for classification:

# Open image
img_folder = "/home/xilinx/jupyter_notebooks/qnn/images/"
img_file = os.path.join(img_folder, max(os.listdir(img_folder), key=lambda f: os.path.getctime(os.path.join(img_folder, f))))
img, img_class = classifier.load_image(img_file)
im = Image.open(img_file)
im

# Execute first layer
conv0_W = conv0_weights['conv0/W']
conv0_T = conv0_weights['conv0/T']

start = datetime.now()
# 1st convolutional layer execution, having as input the image and the trained parameters (weights)
conv0 = utils.conv_layer(img, conv0_W, stride=4)
# The result in then quantized to 2 bits representation for the subsequent HW offload
conv0 = utils.threshold(conv0, conv0_T)

# Allocate accelerator output buffer
end = datetime.now()
micros = int((end - start).total_seconds() * 1000000)
print("First layer SW implementation took {} microseconds".format(micros))
print(micros, file=open('timestamp.txt', 'w'))

# Compute offloaded convolutional layers
out_dim = net['merge4']['output_dim']
out_ch = net['merge4']['output_channels']

conv_output = classifier.get_accel_buffer(out_ch, out_dim);
conv_input = classifier.prepare_buffer(conv0)

start = datetime.now()
classifier.inference(conv_input, conv_output)
end = datetime.now()

micros = int((end - start).total_seconds() * 1000000)
print("HW implementation took {} microseconds".format(micros))
print(micros, file=open('timestamp.txt', 'a'))

conv_output = classifier.postprocess_buffer(conv_output)

# Normalize results
fc_input = conv_output / np.max(conv_output)

start = datetime.now()

# FC Layer 0
fc0_W = fc_weights['fc0/Wn']
fc0_b = fc_weights['fc0/bn']

fc0_out = utils.fully_connected(fc_input, fc0_W, fc0_b)
fc0_out = utils.qrelu(fc0_out)
fc0_out = utils.quantize(fc0_out, 2)

# FC Layer 1
fc1_W = fc_weights['fc1/Wn']
fc1_b = fc_weights['fc1/bn']

fc1_out = utils.fully_connected(fc0_out, fc1_W, fc1_b)
fc1_out = utils.qrelu(fc1_out)

# FC Layer 2
fct_W = fc_weights['fct/W']
fct_b = np.zeros((fct_W.shape[1], ))

fct_out = utils.fully_connected(fc1_out, fct_W, fct_b)
end = datetime.now()
micros = int((end - start).total_seconds() * 1000000)
print("Fully-connected layers took {} microseconds".format(micros))
print(micros, file=open('timestamp.txt', 'a'))

 

I tested the network with five images.  The shark image was included with the notebook and I used the 2 dog and 2 puppy images downloaded from the Internet that I had used to test the CIFAR-10 binary network.  Here are the results

 

Shark

Classification Result:

Execution time:

  • First layer SW implementation too 654851 microseconds
  • HW implementation took 79813 microseconds
  • Fully-connected layers took 569449 microseconds

     Total execution time: 1304113 microseconds

 

Full SW implementation execution time:

The network was also tested with the middle HW layer implemented in SW to determine the impact of the HW implementation.

 

     Total execution time: 397517703

 

The network with the middle HW layer is about 300x faster!

 

The execution time profile is approximately the same for all the images, so I'll only provide the classification results for the rest of the images.

 

Dog1

Classification Result:

Dog2

Classification Result:

Puppy1

Classification Result:

Puppy2

Classification Result:

 

The Classifier struggled a bit with the puppies but I'll admit that I'm not sure what breeds (could be mixed) that they are either.  I was impressed by how well it did with the other images.

 

Time to move on to what I really want to do.....  object detection and identification within an image.