Inferencing on the trained caffe model from NVIDIA DIGITS

With this post I will explain how to do inferencing on the trained network created with NVIDIA DIGITS through command line. link to the previous post here

In DIGITS UI, we have to upload a file into the model webpage to do inferencing. This is time consuming and not practical for real world appications. We need to deploy trained model as a standalone python application.
To achieve this we need to download the trained model from NVIDIA DIGITS model page. This will download a .tgz file to your computer. Open the .tgz file using this command

tar -xvzf filename.tgz
caffe model NVIDIA DIGITS

Save the ‘Image mean’ image file from datasets page of NVIDIA DIGITS in to your computer.

NVIDIA DIGITS inferencing

Provide path for,

'Image mean' file    -> eg:'/home/catsndogs/mean.jpg'
deploy.prototext ->eg:'/home/catsndogs/deploy.prototxt'
caffemodel ->eg:'/home/catsndogs/snapshot_iter_480.caffemodel'
input image to test ->eg:'/home/catsndogs/image_to_test.jpg'

in the below python script.

import numpy as np
import matplotlib.pyplot as plt
import caffe
import time
from PIL import Image

MODEL_FILE = '/home/catsndogs/deploy.prototxt'
PRETRAINED = '/home/catsndogs/snapshot_iter_480.caffemodel'
MEAN_IMAGE = '/home/catsndogs/mean.jpg'
# load the mean image
mean_image =
#input the image file need to be tested
IMAGE_FILE = '/home/catsndogs/image_to_test.jpg'
im1 =
# Tell Caffe to use the GPU
# Initialize the Caffe model using the model trained in DIGITS
net = caffe.Classifier(MODEL_FILE, PRETRAINED,
image_dims=(256, 256))
# Load the input image into a numpy array and display it
# Iterate over each grid square using the model to make a class prediction
start = time.time()
inferImg = im1.resize((256, 256), Image.NEAREST)
inferImg -= mean_image
prediction = net.predict([inferImg])
end = time.time()
if pred == 0: 
# Display total time to perform inference
print 'Total inference time: ' + str(end-start) + ' seconds'

Run the file with


for inferencing.

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