Using Mxnet model to predict

MxNetModel.py

#! /usr/bin/env python
#################################################################################
#     File Name           :     test-one.py
#     Created By          :     Hao Li
#     Creation Date       :     [2016-04-08 13:44]
#     Last Modified       :     [2016-04-26 13:49]
#     Description         :      
#################################################################################

import os
import sys
import time
import numpy 
import LabelIndex
from skimage import io,transform
import logging
import numpy as np
sys.path.insert(0, "/mnt/disk0/lihao/plate-test/libs")
import mxnet as mx

class MxnetModel():
    
    def convert_mean_file(self, mean_filename):
        pass

    def get_mean(self, mean_filename):
        return npy.mean(1).mean(1)


    def __init__(self, data_name="current"):
        mxnet_judge_rounds = 70
        mx_net_judge_prefix = "/mnt/disk0/lihao/plate-test/data/mxnet-model/platenet-100-0"
        epoch = 70
        device_id = 0
        self.model = mx.model.FeedForward.load(mx_net_judge_prefix, int(mxnet_judge_rounds),
                ctx=mx.gpu(device_id), numpy_batch_size=1)

    def predict(self, segments):
        resized_imgs = [transform.resize(io.imread(path), (64,64)) for path in segments]
        swapped_imgs = np.swapaxes(np.swapaxes(np.asarray(resized_imgs) * 256, 1, 3), 2, 3)
        probs = self.model.predict(swapped_imgs)
        return probs