def itml_cmc(features, train_idxs, query_idxs, camId, gallery_idxs, labels):
N, m = features[train_idxs].shape
# features[train_idxs][:1000, ].shape
eigvals, eigvecs = calc_eig_pca_small(features[train_idxs].T, m, N)
m = 50
m_eigvecs = eigvecs[:, :m]
avg_face = compute_avg_face(features[train_idxs].T)
phi = features - avg_face
m_features = np.dot(phi, m_eigvecs)
itml = ITML_Supervised(verbose=True, num_constraints=5000, gamma=0.2)
X = m_features[train_idxs]
Y = labels[train_idxs]
X_itml = itml.fit_transform(X, Y)
M = itml.metric()
nn_idx_mat = evaluation(
knn,
features=m_features,
gallery_idxs=gallery_idxs,
query_idxs=query_idxs,
camId=camId,
labels=labels,
metric='mahalanobis',
metric_params={'VI': M}
)
return plot_CMC(nn_idx_mat, query_idxs, labels)
def main():
# Get training file name from the command line
traindatafile = sys.argv[1]
# The training file is in libSVM format
tr_data = load_svmlight_file(traindatafile)
Xtr = tr_data[0].toarray()
# Converts sparse matrices to dense
Ytr = tr_data[1]
# The trainig labels
Indices_array = np.arange(Ytr.shape[0])
np.random.shuffle(Indices_array)
Xtr = Xtr[Indices_array]
Xtr = Xtr[:6000]
Ytr = Ytr[Indices_array]
Ytr = Ytr[:6000]
itml = ITML_Supervised()
itml.fit(Xtr, Ytr)
Met = itml.metric()
# print Met;
np.save("itml_model.npy", Met)
def get_metric():
ad=pd.read_csv('ad_feature.csv',header=0,sep='\t')
data=ad.values
m=np.array([[.0,.0,.0],[.0,.0,.0],[.0,.0,.0]])
itml = ITML_Supervised(num_constraints=200)
for i in range(6):
data_r=np.array(random.sample(data.tolist(),int(len(data)/300)))
x=data_r[:,[2,3,4]]
y=data_r[:,1]
itml.fit(x,y)
m=m+itml.metric()
m=m/6
return m
def test_itml_supervised(self): seed = np.random.RandomState(1234) itml = ITML_Supervised(num_constraints=200) itml.fit(self.X, self.y, random_state=seed) L = itml.transformer_ assert_array_almost_equal(L.T.dot(L), itml.metric())
def main():
print("importing data...")
data = loadmat('assets/cuhk03_new_protocol_config_labeled.mat')
with open('assets/feature_data.json') as f:
features = ujson.load(f)
print("data imported")
features = np.array(features)
train_idxs = data['train_idx'].flatten() - 1
query_idxs = data['query_idx'].flatten() - 1
camId = data['camId'].flatten()
gallery_idxs = data['gallery_idx'].flatten() - 1
labels = data['labels'].flatten()
N, m = features[train_idxs].shape
# features[train_idxs][:1000, ].shape
eigvals, eigvecs = calc_eig_pca_small(features[train_idxs].T, m, N)
m = 50
m_eigvecs = eigvecs[:, :m]
avg_face = compute_avg_face(features[train_idxs].T)
phi = features - avg_face
m_features = np.dot(phi, m_eigvecs)
itml = ITML_Supervised(verbose=True, num_constraints=5000, gamma=0.1)
X = m_features[train_idxs]
Y = labels[train_idxs]
X_itml = itml.fit_transform(X, Y)
M = itml.metric()
plot_3d(X_itml, Y)
nn_idx_mat = evaluation(knn,
features=m_features,
gallery_idxs=gallery_idxs,
query_idxs=query_idxs,
camId=camId,
labels=labels,
metric='mahalanobis',
metric_params={'VI': M})
acc = get_all_rank_acc(nn_idx_mat, query_idxs, labels)
print("Accuracy:")
print(acc)
test_set_idxs = np.append(gallery_idxs, query_idxs)
features_ITML = itml.transform(m_features)
X_test = features_ITML[test_set_idxs]
Y_test = labels[test_set_idxs]
n_cluster = np.unique(Y_test).size
nmi_kmean, acc_kmean = evaluation_k_means(X_test, n_cluster, Y_test)
print("ITML k-means accuracy (test set):")
print(acc_kmean)
gamma = [i / 10 for i in range(1, 11)]
X_itmls = []
all_rank_acc_g = []
for g in gamma:
itml = ITML_Supervised(verbose=True, num_constraints=5000, gamma=0.2)
X = m_features[train_idxs]
X_itml = itml.fit_transform(X, Y)
X_itmls.append(X_itml)
M = itml.metric()
nn_idx_mat = evaluation(knn,
features=m_features,
gallery_idxs=gallery_idxs,
query_idxs=query_idxs,
camId=camId,
labels=labels,
metric='mahalanobis',
metric_params={'VI': M})
acc_g = get_all_rank_acc(nn_idx_mat, query_idxs, labels)
all_rank_acc_g.append(acc_g)
plt.plot(gamma, all_rank_acc_g)
plt.legend(('Rank 1', 'Rank 5', 'Rank10'))
plt.ylabel('Accuracy')
plt.xlabel('gamma')
print(all_rank_acc_g)
plt.show()
def gettestData():
# Get testing file name from the command line
testdatafile = sys.argv[2]
# The testing file is in libSVM format
ts_data = load_svmlight_file(testdatafile)
Xts = ts_data[0].toarray() # Converts sparse matrices to dense
Yts = ts_data[1] # The trainig labels
return Xts, Yts
# get training data
Xtr, Ytr = gettrainData()
# get testing data
Xts, Yts = gettestData()
# Taking only a fraction of data. i.e. 1/4th
Xtr = Xtr[:len(Xtr)//4]
Ytr = Ytr[:len(Ytr)//4]
itml = ITML_Supervised(num_constraints=1000)
# learning
itml.fit(Xtr, Ytr)
# Get the learnt metric
M = itml.metric()
# Metric saved
np.save("model.npy", M)
def test_itml_supervised(self): seed = np.random.RandomState(1234) itml = ITML_Supervised(num_constraints=200) itml.fit(self.X, self.y, random_state=seed) L = itml.transformer() assert_array_almost_equal(L.T.dot(L), itml.metric())