Exemplo n.º 1
0
def script_testingDoNN():
    img_paths=[];
    numberOfSamples=100;
    featureVectorLength=10;
    numberOfClasses=5;
    gt_labels=np.random.random_integers(0,numberOfClasses-1,(numberOfSamples,));
    
    features_curr=np.random.random_integers(-100,100,(numberOfSamples,featureVectorLength));
    features_curr=np.array(features_curr,dtype=float);
    numberOfN=5;
    
    indices=script_nearestNeigbourExperiment.doNN(img_paths,gt_labels,features_curr,numberOfN=numberOfN)    
    print indices.shape==(numberOfSamples,numberOfN);
    
    numberOfN=None
    indices=script_nearestNeigbourExperiment.doNN(img_paths,gt_labels,features_curr,numberOfN=numberOfN)
    print indices.shape==(numberOfSamples,numberOfSamples);
    
    indices,conf_matrix=script_nearestNeigbourExperiment.doNN(img_paths,gt_labels,features_curr,numberOfN=numberOfN,conf_matrix_return=True)
    print conf_matrix.shape==(numberOfClasses,numberOfClasses)
    
    indices,distances=script_nearestNeigbourExperiment.doNN(img_paths,gt_labels,features_curr,numberOfN=numberOfN,conf_matrix_return=False,distances_return=True)
    print distances.shape==indices.shape==(numberOfSamples,numberOfSamples);

    indices,conf_matrix,distances=script_nearestNeigbourExperiment.doNN(img_paths,gt_labels,features_curr,numberOfN=numberOfN,conf_matrix_return=True,distances_return=True)
    print conf_matrix.shape==(numberOfClasses,numberOfClasses)
    print distances.shape==indices.shape==(numberOfSamples,numberOfSamples);
    plt.figure();
    plt.plot(distances[0]);
    plt.plot(distances[numberOfSamples-1]);
    plt.savefig('/disk2/temp/checkNN.png');
    plt.close();
Exemplo n.º 2
0
def script_testingDoNN():
    img_paths = []
    numberOfSamples = 100
    featureVectorLength = 10
    numberOfClasses = 5
    gt_labels = np.random.random_integers(0, numberOfClasses - 1,
                                          (numberOfSamples, ))

    features_curr = np.random.random_integers(
        -100, 100, (numberOfSamples, featureVectorLength))
    features_curr = np.array(features_curr, dtype=float)
    numberOfN = 5

    indices = script_nearestNeigbourExperiment.doNN(img_paths,
                                                    gt_labels,
                                                    features_curr,
                                                    numberOfN=numberOfN)
    print indices.shape == (numberOfSamples, numberOfN)

    numberOfN = None
    indices = script_nearestNeigbourExperiment.doNN(img_paths,
                                                    gt_labels,
                                                    features_curr,
                                                    numberOfN=numberOfN)
    print indices.shape == (numberOfSamples, numberOfSamples)

    indices, conf_matrix = script_nearestNeigbourExperiment.doNN(
        img_paths,
        gt_labels,
        features_curr,
        numberOfN=numberOfN,
        conf_matrix_return=True)
    print conf_matrix.shape == (numberOfClasses, numberOfClasses)

    indices, distances = script_nearestNeigbourExperiment.doNN(
        img_paths,
        gt_labels,
        features_curr,
        numberOfN=numberOfN,
        conf_matrix_return=False,
        distances_return=True)
    print distances.shape == indices.shape == (numberOfSamples,
                                               numberOfSamples)

    indices, conf_matrix, distances = script_nearestNeigbourExperiment.doNN(
        img_paths,
        gt_labels,
        features_curr,
        numberOfN=numberOfN,
        conf_matrix_return=True,
        distances_return=True)
    print conf_matrix.shape == (numberOfClasses, numberOfClasses)
    print distances.shape == indices.shape == (numberOfSamples,
                                               numberOfSamples)
    plt.figure()
    plt.plot(distances[0])
    plt.plot(distances[numberOfSamples - 1])
    plt.savefig('/disk2/temp/checkNN.png')
    plt.close()
Exemplo n.º 3
0
def script_saveNNDistances(file_name, layers):
    test_set, _ = pickle.load(open(file_name + '.p', 'rb'))
    vals = np.load(file_name + '.npz')

    test_set = sorted(test_set, key=lambda x: x[0])
    test_set = zip(*test_set)

    img_paths = list(test_set[0])
    gt_labels = list(test_set[1])

    numberOfN = None
    for layer in layers:
        file_name_l = file_name + '_' + layer + '_all_distances'
        indices, conf_matrix = script_nearestNeigbourExperiment.doNN(
            img_paths,
            gt_labels,
            vals[layer],
            numberOfN=numberOfN,
            distance='cosine',
            algo='brute',
            conf_matrix=False,
            distances=True)

        pickle.dump([img_paths, gt_labels, indices, conf_matrix],
                    open(file_name_l + '.p', 'wb'))
Exemplo n.º 4
0
def script_saveNNDistances(file_name,layers):
    test_set,_=pickle.load(open(file_name+'.p','rb'));
    vals=np.load(file_name+'.npz');

    test_set=sorted(test_set,key=lambda x: x[0])
    test_set=zip(*test_set);
    
    img_paths=list(test_set[0]);
    gt_labels=list(test_set[1]);
    
    numberOfN=None;
    for layer in layers:
        file_name_l=file_name+'_'+layer+'_all_distances';
        indices,conf_matrix=script_nearestNeigbourExperiment.doNN(img_paths,gt_labels,vals[layer],numberOfN=numberOfN,distance='cosine',algo='brute',conf_matrix=False,distances=True)

        pickle.dump([img_paths,gt_labels,indices,conf_matrix],open(file_name_l+'.p','wb'));
Exemplo n.º 5
0
def script_runNNOnPascalExcludedInTraining():
    path_to_file = '../../data/ilsvrc12/synset_words.txt'
    val_ids = imagenet.readLabelsFile(path_to_file)
    val_just_ids = list(zip(*val_ids)[0])
    val_just_labels = list(zip(*val_ids)[1])

    pascal_ids_file = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_classes.txt'
    pascal_ids = imagenet.readLabelsFile(pascal_ids_file)
    pascal_just_ids = list(zip(*pascal_ids)[0])

    to_exclude = imagenet.removeClassesWithOverlap(val_just_ids,
                                                   pascal_just_ids,
                                                   keepMapping=True)

    val_gt_file = '../../data/ilsvrc12/val.txt'
    list_of_ids_im = [id for id_list in to_exclude for id in id_list]
    mapping_file = '../../data/ilsvrc12/synsets.txt'
    print len(list_of_ids_im)

    list_of_ids, _ = imagenet.getImagenetIdToTrainingIdMapping(
        mapping_file, list_of_ids_im)
    print len(list_of_ids)
    # print list_of_ids[0]
    list_of_ids_pascal = []

    for id_no in range(len(to_exclude)):
        list_of_ids_pascal = list_of_ids_pascal + [id_no] * len(
            to_exclude[id_no])

    path_to_val = '/disk2/imagenet/val'
    test_set = imagenet.selectTestSetByID(val_gt_file, list_of_ids,
                                          path_to_val)

    # out_dir='/disk2/octoberExperiments/nn_performance_without_pascal/notrained'
    out_dir = '/disk2/novemberExperiments/nn_imagenet_top5/trained'
    if not os.path.exists(out_dir):
        os.mkdir(out_dir)

    layers = ['pool5', 'fc6', 'fc7']
    gpu_no = 1
    path_to_classify = '..'
    numberOfN = 5
    relativePaths = ['/disk2', '../../../..']

    # deployFile='/disk2/octoberExperiments/nn_performance_without_pascal/deploy.prototxt'
    # meanFile='/disk2/octoberExperiments/nn_performance_without_pascal/mean.npy'
    # modelFile='/disk2/octoberExperiments/nn_performance_without_pascal/snapshot_iter_450000.caffemodel'

    modelFile = '/home/maheenrashid/Downloads/caffe/caffe-rc2/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
    deployFile = '/home/maheenrashid/Downloads/caffe/caffe-rc2/models/bvlc_reference_caffenet/deploy.prototxt'
    meanFile = '/home/maheenrashid/Downloads/caffe/caffe-rc2/python/caffe/imagenet/ilsvrc_2012_mean.npy'

    # modelFile='/disk2/novemberExperiments/network_no_pascal/snapshots/snapshot_iter_450000.caffemodel';
    # deployFile='/disk2/novemberExperiments/network_no_pascal/deploy.prototxt';
    # meanFile='/disk2/novemberExperiments/network_no_pascal/mean.npy';

    # out_file=script_nearestNeigbourExperiment.runClassificationTestSet(test_set,out_dir,path_to_classify,gpu_no,layers,deployFile=deployFile,meanFile=meanFile,modelFile=modelFile)

    # return
    # file_name='/disk2/octoberExperiments/nn_performance_without_pascal/notrained/20151026132705'
    # file_name='/disk2/novemberExperiments/nn_imagenet_top5/notrained/20151130193757';
    file_name = '/disk2/novemberExperiments/nn_imagenet_top5/trained/20151130230243'
    file_text_labels = '../../data/ilsvrc12/synset_words.txt'

    text_labels = np.loadtxt(file_text_labels, str, delimiter='\t')

    vals = np.load(file_name + '.npz')

    test_set = sorted(test_set, key=lambda x: x[0])
    test_set = zip(*test_set)
    img_paths = list(test_set[0])
    gt_labels = list(test_set[1])
    gt_labels_pascal = [
        list_of_ids_pascal[list_of_ids.index(gt_label)]
        for gt_label in gt_labels
    ]

    for layer in layers:
        print layer
        file_name_l = file_name + '_' + layer
        indices = script_nearestNeigbourExperiment.doNN(img_paths,
                                                        gt_labels,
                                                        vals[layer],
                                                        numberOfN=numberOfN,
                                                        distance='cosine',
                                                        algo='brute')
        conf_matrix = 0
        pickle.dump([img_paths, gt_labels, indices, conf_matrix],
                    open(file_name_l + '.p', 'wb'))

    file_text_labels_pascal = '/disk2/octoberExperiments/nn_performance_without_pascal/pascal_classes.txt'
    text_labels_pascal = np.loadtxt(file_text_labels_pascal,
                                    str,
                                    delimiter='\t')

    for layer in layers:
        print layer
        file_name_l = file_name + '_' + layer
        [img_paths, gt_labels, indices,
         _] = pickle.load(open(file_name_l + '.p', 'rb'))
        img_paths_curr = [
            x.replace(relativePaths[0], relativePaths[1]) for x in img_paths
        ]
        im_paths, captions = script_nearestNeigbourExperiment.createImageAndCaptionGrid(
            img_paths_curr, gt_labels, indices, text_labels)
        script_nearestNeigbourExperiment.writeHTML(file_name_l + '.html',
                                                   im_paths, captions)
        no_correct, _ = script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(
            indices, gt_labels)
        print no_correct
        with open(file_name_l + '.txt', 'wb') as f:
            for no_correct_curr in no_correct:
                f.write(str(no_correct_curr) + ' ')

        file_name_l = file_name + '_' + layer + '_pascal'
        im_paths, captions = script_nearestNeigbourExperiment.createImageAndCaptionGrid(
            img_paths_curr, gt_labels_pascal, indices, text_labels_pascal)
        script_nearestNeigbourExperiment.writeHTML(file_name_l + '.html',
                                                   im_paths, captions)
        no_correct, _ = script_nearestNeigbourExperiment.getNumberOfCorrectNNMatches(
            indices, gt_labels_pascal)
        with open(file_name_l + '.txt', 'wb') as f:
            for no_correct_curr in no_correct:
                f.write(str(no_correct_curr) + ' ')

        print no_correct