def test(list_algo_name, list_bits):
root = '../../ILSVRC2012_caffe/npy_data/'
baselabel = np.load(root+'trainlabel.npy')
testlabel = np.load(root+'testlabel.npy')
testdata = np.load(root+'testdata_fc6_norm.npy')
basicdata = np.load(root+'traindata_fc6_norm_1.npy')
seeds = [7]#, 17, 37, 47, 67, 97, 107, 127, 137, 157]
for algo_name in list_algo_name:
for nbit in list_bits:
print '======execute {} at bit {}======'.format(algo_name, nbit)
print '====total process round: {}====='.format(len(seeds))
li_results = []
for sd in seeds:
print '\nround #{}...'.format(len(li_results)+1)
# load data
np.random.seed(sd)
idx = np.arange(100000, dtype=np.int32)
np.random.shuffle(idx)
traindata = basicdata[idx]
trainlabel = baselabel[idx]
alg = hash_factory(algo_name, nbit, 1000, 1000)
tic = time.clock()
alg.train(traindata, trainlabel)
toc = time.clock()
print 'time:', toc-tic
print 'hash testing data...'
H_test = alg.queryhash(testdata)
print 'hash training data...'
H_base = np.zeros((baselabel.shape[0], H_test.shape[1]), dtype=np.uint8)
pt = 0
for i in xrange(13):
print 'hashing', i, '...'
X = np.load('traindata_fc6_norm_{}.npy'.format(i+1))
n = X.shape[0]
H_base[pt:pt+n] = alg.basehash(X)
pt += n
print 'testing...'
batch_results = []
for i in xrange(50):
# make labels
print 'testing batch', i, '...'
gnd_truth = np.array([y == baselabel for y in testlabel[i*1000:(i+1)*1000]]).astype(np.int8)
batch_results.append(hash_evaluation(H_test, H_base, gnd_truth, 0, topN=5000, trn_time=toc-tic))
eva_checkpoint(algo_name, nbit, batch_results)
li_results.append(batch_eva_ensem(batch_results))
eva_checkpoint(algo_name, nbit, li_results)
def batch_checkpoint(algo_name, nbit, li_results):
dump('results_imagenet/{}_{}_batch'.format(algo_name, nbit), li_results)
res = batch_eva_ensem(li_results)
print 'mean: mAP={}, pre2={}'.format(res['map'], res['pre2'])
