interleavingdemo.py

#!/usr/bin/python3

import sys
sys.path.insert(0, '../../')
import microndla
import sys
import threading
import os
import PIL
from PIL import Image
import numpy as np
from time import sleep

from argparse import ArgumentParser
# argument Checking
parser = ArgumentParser(description="Micron DLA Example")
_ = parser.add_argument
_('modelpath', type=str, default='', help='Path to the model file')
_('imagesdir', type=str, default='', help='A directory name with input files')
_('-r', '--res', type=int, default=[3, 224, 224], nargs='+', help='expected image size (planes, height, width)')
_('-c', '--categories', type=str, default='', help='Categories file')

def LoadImage(imagepath):

    #Load image into a numpy array
    img = Image.open(imagepath)

    #Resize it to the size expected by the network
    img = img.resize((xres, yres), resample=PIL.Image.BILINEAR)

    #Convert to numpy float
    img = np.array(img).astype(np.float32) / 255

    #Transpose to plane-major, as required by our API
    img = np.ascontiguousarray(img.transpose(2,0,1))

    #Normalize images
    stat_mean = list([0.485, 0.456, 0.406])
    stat_std = list([0.229, 0.224, 0.225])
    for i in range(3):
        img[i] = (img[i] - stat_mean[i])/stat_std[i]

    return img

args = parser.parse_args()

xres = args.res[2]
yres = args.res[1]

#Create and initialize the snowflow object
ie = microndla.MDLA()
#ie.SetFlag('debug','bw')

#Compile to a file
swnresults = ie.Compile(args.modelpath)

categories = None
if args.categories != '':
    with open(args.categories) as f:
        categories = f.read().splitlines()

#Create the storage for the result and run one inference
nimages = 0

def getresult():
    result, imgname = ie.GetResult()
    result = np.squeeze(result, axis=0)
    #Convert to numpy and print top-5
    idxs = (-result).argsort()

    print('')
    print('-------------- ' + str(imgname) + ' --------------')
    if categories != None:
        for i in range(5):
            print(categories[idxs[i]], result[idxs[i]])
    else:
        for i in range(5):
            print(idxs[i], result[idxs[i]])

for fn in os.listdir(args.imagesdir):
    try:
        img = LoadImage(args.imagesdir + '/' + fn)
    except:
        pass
    ie.PutInput(img, fn)
    nimages += 1
    if nimages > 1:
        getresult()

if nimages > 0:
    getresult()

#Free
ie.Free()
print('done')