5/15/2017 - 12:36 AM

eval.py Deploying the classifier on the testing dataset

eval.py Deploying the classifier on the testing dataset

from __future__ import print_function
import lmdb
import caffe
import numpy as np
import csv

__author__ = 'PedroMorgado'

def load_lmdb(fn):
    env = lmdb.open(fn, readonly=True)
    datum = caffe.proto.caffe_pb2.Datum()
    with env.begin() as txn:
        cursor = txn.cursor()
        data, labels = [], []
        for _, value in cursor:
    return data, labels

def prep_image(img):
    img = img.astype(float)[:, 14:241, 14:241]             # center crop (img is in shape [C, X, Y])
    img -= np.array([104., 117., 123.]).reshape((3,1,1))     # demean (same as in trainval.prototxt
    return img

def main(test_data, num_class):
    gpu_id = 1
    images, labels = load_lmdb(test_data)

    deploy = caffe.Net('caffenet/deploy.prototxt', caffe.TEST, weights='model.caffemodel')
    probs = []
    for i in range(0, len(images), 25):
        batch = [prep_image(img) for img in images[i:i+25]]
        batch_size = len(batch)

        deploy.blobs['data'].data[:batch_size] = batch
        probs.append(np.copy(deploy.blobs['prob'].data[:batch_size, :]))    # Note np.copy. Otherwise, next forward() step will replace memory

    print ('probs list length:', len(probs))
    print ('probs element type:', type(probs[0]))
    print (probs[0])

    probs = np.concatenate(probs, 0)

    print ('probs shape after concatenate:', probs.shape)
    print (probs[0,:], type(probs[0,0]))

    # compute accuracy
    predictions = probs.argmax(1)
    gtruth = np.array(labels)
    total_accu = (predictions == gtruth).mean()*100

    print ('predictions shape:', predictions.shape)
    print (predictions[0:25])
    print('Total Accuracy', total_accu)

    # write results to a txt file
    results_txt = open('Image_preds.txt', 'w')
    for i in range(len(gtruth)):
        results_txt.write(str(probs[i,0])+' '+str(probs[i,1]))
        results_txt.write(' ')

    # compute confusion matrix
    class_count = np.zeros((num_class, 1))  # 1st col is number of images in each class
    pred_count = np.zeros((num_class, num_class))  # each row is for one class, each col is the num of pred from row class to one of the classes
    for i in range(len(gtruth)):
        class_count[gtruth[i],0] += 1.
        pred_count[gtruth[i],predictions[i]] += 1.
    confusion_mat = np.zeros((num_class, num_class))
    for i in range(num_class):
        confusion_mat[i,:] = (pred_count[i,:])/class_count[i,0]
    confusion_mat = np.around(confusion_mat, decimals=4)
    print ('Prediction Results:')
    print (pred_count)
    print ('Confusion Matrix:')
    print (confusion_mat*100)
    filename = test_data+'_Results.csv'
    outfile = open(filename, 'wb')
    writer = csv.writer(outfile, delimiter=",")
    writer.writerow(['Class classification on the different specimen'])
    writer.writerow(['Total Accuracy'])
    writer.writerow(['Prediction Results:'])
    np.savetxt(outfile, pred_count, delimiter=",")
    writer.writerow(['Confusion Matrix:'])
    np.savetxt(outfile, confusion_mat*100, delimiter=",")
    print ('Print to', filename, 'file successful.')

if __name__ == '__main__':
    main('test1.LMDB', 2)