PRCurve.py

#!/usr/bin/env python
# coding=utf-8

from __future__ import absolute_import
from __future__ import print_function, division

import warnings

import os
import numpy as np
import tensorflow as tf
from DPQ import DPQ, IMAGE_WIDTH, IMAGE_HEIGHT
from Dataset import Dataset

warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=DeprecationWarning)

tf.logging.set_verbosity(tf.logging.ERROR)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string("Dataset", "NUS", "The preferred dataset, \'CIFAR\', \'NUS\' or \'Imagenet\'")
tf.app.flags.DEFINE_string("Mode", "eval", "\'train\' or \'eval\'")
tf.app.flags.DEFINE_integer("BitLength", 32, "Binary code length")
tf.app.flags.DEFINE_integer("ClassNum", 21, "Label num of dataset")
tf.app.flags.DEFINE_integer("K", 256, "The centroids number of a codebook")
tf.app.flags.DEFINE_integer("PrintEvery", 50, "Print every ? iterations")
tf.app.flags.DEFINE_float("LearningRate", 1e-4, "Init learning rate")
tf.app.flags.DEFINE_integer("Epoch", 64, "Total epoches")
tf.app.flags.DEFINE_integer("BatchSize", 256, "Batch size")
tf.app.flags.DEFINE_string("Device", "0", "GPU device ID")
tf.app.flags.DEFINE_boolean("UseGPU", True, "Use /device:GPU or /cpu")
tf.app.flags.DEFINE_boolean("SaveModel", True, "Save model at every epoch done")
tf.app.flags.DEFINE_integer("R", 5000, "mAP@R, -1 for all")
tf.app.flags.DEFINE_float("Lambda", 0.1, "Lambda, decribed in paper")
tf.app.flags.DEFINE_float("Tau", 1, "Tau, decribed in paper")
tf.app.flags.DEFINE_float("Mu", 1, "Mu, decribed in paper")
tf.app.flags.DEFINE_float("Nu", 0.1, "Nu, decribed in paper")

os.environ['CUDA_VISIBLE_DEVICES'] = str(FLAGS.Device)


def main(_):
    model = DPQ(FLAGS)
    a = "/device:GPU:0" if FLAGS.UseGPU else "/cpu:0"
    print("Using device:", a, "<-", FLAGS.Device)
    with tf.device(a):
        queryX, queryY, db = Dataset.PreparetoEval(FLAGS.Dataset, IMAGE_WIDTH, IMAGE_HEIGHT)
        fileName = model.GetRetrievalMat(queryX, queryY, db)
    if os.path.exists(fileName):
        retrievalMat = np.load(fileName)
        precision = np.mean(np.cumsum(retrievalMat, axis=1) / np.arange(1, retrievalMat.shape[1] + 1, 1), axis=0)
        np.savetxt('precision.csv', precision)
        totalResult = np.sum(retrievalMat, axis=1)
        print(totalResult)
        # [Nq, Nb] / [Nq, 1]
        recall = np.mean(np.cumsum(retrievalMat, axis=1) / totalResult[:, None], axis=0)
        np.savetxt('recall.csv', recall)


if __name__ == '__main__':
    tf.app.run()