Args:
dataset: The dataset returned by ``compute_attr``
Returns:
The pairwise distance matrix D
"""
from sklearn.metrics.pairwise import pairwise_distances as pwdist
gA, gY, pA, pY = dataset
return pwdist(gA, pA, 'euclidean')
if __name__ == '__main__':
data = load_data(['CUHKL'])
data = decompose(data)
dataset = preprocess(data)
import cPickle
with open('../cache/attr_model.pkl', 'rb') as f:
model = cPickle.load(f)
dataset = compute_attr(model, dataset)
D = compute_distance(dataset)
from reid.utils import cmc
cmcret = cmc.count(D, dataset[1], dataset[3], 100)
print cmcret
R[i] = output_func(X[i:i + 1, :]).ravel()
R = numpy.asarray(R)
D = cdist(R, Y, 'euclidean')
G = numpy.asarray([pid for pid, image in data[0]])
P = numpy.asarray([pid for pid, image in data[1]])
if save_to_cache:
with open(_cached_distmat, 'wb') as f:
cPickle.dump((D, G, P), f, protocol=cPickle.HIGHEST_PROTOCOL)
with open(_cached_output, 'wb') as f:
cPickle.dump((X, Y, R), f, protocol=cPickle.HIGHEST_PROTOCOL)
return (D, G, P)
if __name__ == '__main__':
views_data, datasets = _prepare_data(load_from_cache=True,
save_to_cache=False)
model = _train_model(datasets, load_from_cache=False, save_to_cache=True)
distmat, glabels, plabels = _get_distance(model,
views_data,
load_from_cache=False,
save_to_cache=True)
print cmc.count(distmat, glabels, plabels, 100)
for i in xrange(X.shape[0]):
R[i] = output_func(X[i:i+1, :]).ravel()
R = numpy.asarray(R)
D = cdist(R, Y, 'euclidean')
G = numpy.asarray([pid for pid, image in data[0]])
P = numpy.asarray([pid for pid, image in data[1]])
if save_to_cache:
with open(_cached_distmat, 'wb') as f:
cPickle.dump((D, G, P), f, protocol=cPickle.HIGHEST_PROTOCOL)
with open(_cached_output, 'wb') as f:
cPickle.dump((X, Y, R), f, protocol=cPickle.HIGHEST_PROTOCOL)
return (D, G, P)
if __name__ == '__main__':
views_data, datasets = _prepare_data(load_from_cache=True,
save_to_cache=False)
model = _train_model(datasets, load_from_cache=False, save_to_cache=True)
distmat, glabels, plabels = _get_distance(model, views_data,
load_from_cache=False,
save_to_cache=True)
print cmc.count(distmat, glabels, plabels, 100)
Args:
dataset: The dataset returned by ``compute_attr``
Returns:
The pairwise distance matrix D
"""
from sklearn.metrics.pairwise import pairwise_distances as pwdist
gA, gY, pA, pY = dataset
return pwdist(gA, pA, 'euclidean')
if __name__ == '__main__':
data = load_data(['CUHKL'])
data = decompose(data)
dataset = preprocess(data)
import cPickle
with open('../cache/attr_model.pkl', 'rb') as f:
model = cPickle.load(f)
dataset = compute_attr(model, dataset)
D = compute_distance(dataset)
from reid.utils import cmc
cmcret = cmc.count(D, dataset[1], dataset[3], 100)
print cmcret
