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OSLPP.py
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OSLPP.py
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import math
from sklearn.decomposition import PCA
import scipy
import numpy as np
import scipy.io
import scipy.linalg
from dataclasses import dataclass
def _load_tensors(domain):
mapping = {
'art': 'Art',
'clipart': 'Clipart',
'product': 'Product',
'real_world': 'RealWorld'
}
mat = scipy.io.loadmat(f'mats/OfficeHome-{mapping[domain]}-resnet50-noft.mat')
features, labels = mat['resnet50_features'], mat['labels']
features, labels = features[:,:,0,0], labels[0]
assert len(features) == len(labels)
# features, labels = torch.tensor(features), torch.tensor(labels)
# features = torch.load(f'./data_handling/features/OH_{domain}_features.pt')
# labels = torch.load(f'./data_handling/features/OH_{domain}_labels.pt')
return features, labels
def create_datasets(source, target, num_src_classes, num_total_classes):
src_features, src_labels = _load_tensors(source)
idxs = src_labels < num_src_classes
src_features, src_labels = src_features[idxs], src_labels[idxs]
tgt_features, tgt_labels = _load_tensors(target)
idxs = tgt_labels < num_total_classes
tgt_features, tgt_labels = tgt_features[idxs], tgt_labels[idxs]
tgt_labels[tgt_labels >= num_src_classes] = num_src_classes
assert (np.unique(src_labels) == np.arange(0, num_src_classes)).all()
assert (np.unique(tgt_labels) == np.arange(0, num_src_classes+1)).all()
assert len(src_features) == len(src_labels)
assert len(tgt_features) == len(tgt_labels)
return (src_features, src_labels), (tgt_features, tgt_labels)
def get_l2_norm(features:np.ndarray): return np.sqrt(np.square(features).sum(axis=1)).reshape((-1,1))
def get_l2_normalized(features:np.ndarray): return features / get_l2_norm(features)
def get_PCA(features, dim):
result = PCA(n_components=dim).fit_transform(features)
assert len(features) == len(result)
return result
def get_W(labels,):
W = (labels.reshape(-1,1) == labels).astype(np.int)
negative_one_idxs = np.where(labels == -1)[0]
W[:,negative_one_idxs] = 0
W[negative_one_idxs,:] = 0
return W
def get_D(W): return np.eye(len(W), dtype=np.int) * W.sum(axis=1)
def fix_numerical_assymetry(M): return (M + M.transpose()) * 0.5
def get_projection_matrix(features, labels, proj_dim):
N, d = features.shape
X = features.transpose()
W = get_W(labels)
D = get_D(W)
L = D - W
A = fix_numerical_assymetry(np.matmul(np.matmul(X, D), X.transpose()))
B = fix_numerical_assymetry(np.matmul(np.matmul(X, L), X.transpose()) + np.eye(d))
assert (A.transpose() == A).all() and (B.transpose() == B).all()
w, v = scipy.linalg.eigh(A, B)
assert w[0] < w[-1]
w, v = w[-proj_dim:], v[:, -proj_dim:]
assert np.abs(np.matmul(A, v) - w * np.matmul(B, v)).max() < 1e-5
w = np.flip(w)
v = np.flip(v, axis=1)
for i in range(v.shape[1]):
if v[0,i] < 0:
v[:,i] *= -1
return v
def project_features(P, features):
# P: pca_dim x proj_dim
# features: N x pca_dim
# result: N x proj_dim
return np.matmul(P.transpose(), features.transpose()).transpose()
def get_centroids(features, labels):
centroids = np.stack([features[labels == c].mean(axis=0) for c in np.unique(labels)], axis=0)
centroids = get_l2_normalized(centroids)
return centroids
def get_dist(f, features):
return get_l2_norm(f - features)
def get_closed_set_pseudo_labels(features_S, labels_S, features_T):
centroids = get_centroids(features_S, labels_S)
dists = np.stack([get_dist(f, centroids)[:,0] for f in features_T], axis=0)
pseudo_labels = np.argmin(dists, axis=1)
pseudo_probs = np.exp(-dists[np.arange(len(dists)), pseudo_labels]) / np.exp(-dists).sum(axis=1)
return pseudo_labels, pseudo_probs
def select_initial_rejected(pseudo_probs, n_r):
is_rejected = np.zeros((len(pseudo_probs),), dtype=np.int)
is_rejected[np.argsort(pseudo_probs)[:n_r]] = 1
return is_rejected
def select_closed_set_pseudo_labels(pseudo_labels, pseudo_probs, t, T):
if t >= T: t = T - 1
selected = np.zeros_like(pseudo_labels)
for c in np.unique(pseudo_labels):
idxs = np.where(pseudo_labels == c)[0]
Nc = len(idxs)
if Nc > 0:
class_probs = pseudo_probs[idxs]
class_probs = np.sort(class_probs)
threshold = class_probs[math.floor(Nc*(1-t/(T-1)))]
idxs2 = idxs[pseudo_probs[idxs] > threshold]
assert (selected[idxs2] == 0).all()
selected[idxs2] = 1
return selected
def update_rejected(selected, rejected, features_T):
unlabeled = (selected == 0) * (rejected == 0)
new_is_rejected = rejected.copy()
for idx in np.where(unlabeled)[0]:
dist_to_selected = get_dist(features_T[idx], features_T[selected == 1]).min()
dist_to_rejected = get_dist(features_T[idx], features_T[rejected == 1]).min()
if dist_to_rejected < dist_to_selected:
new_is_rejected[idx] = 1
return new_is_rejected
def evaluate(predicted, labels, num_src_classes):
acc_unk = (predicted[labels == num_src_classes] == labels[labels == num_src_classes]).mean()
accs = [(predicted[labels == c] == labels[labels == c]).mean() for c in range(num_src_classes)]
acc_common = np.array(accs).mean()
hos = 2 * acc_unk * acc_common / (acc_unk + acc_common)
_os = np.array(accs+[acc_unk]).mean()
return f'OS={_os*100:.2f} OS*={acc_common*100:.2f} unk={acc_unk*100:.2f} HOS={hos*100:.2f}'
@dataclass
class Params:
pca_dim: int # = 512
proj_dim: int # = 128
T: int # = 10
n_r: int # = 1200
dataset: str # = 'OfficeHome'
source: str # = 'art'
target: str # = 'clipart'
num_src_classes: int # = 25
num_total_classes: int # = 65
def do_l2_normalization(feats_S, feats_T):
feats_S, feats_T = get_l2_normalized(feats_S), get_l2_normalized(feats_T)
assert np.abs(get_l2_norm(feats_S) - 1.).max() < 1e-5
assert np.abs(get_l2_norm(feats_T) - 1.).max() < 1e-5
return feats_S, feats_T
def do_pca(feats_S, feats_T, pca_dim):
feats = np.concatenate([feats_S, feats_T], axis=0)
feats = get_PCA(feats, pca_dim)
feats_S, feats_T = feats[:len(feats_S)], feats[len(feats_S):]
return feats_S, feats_T
def center_and_l2_normalize(zs_S, zs_T):
# center
zs_mean = np.concatenate((zs_S, zs_T), axis=0).mean(axis=0).reshape((1,-1))
zs_S = zs_S - zs_mean
zs_T = zs_T - zs_mean
# l2 normalize
zs_S, zs_T = do_l2_normalization(zs_S, zs_T)
return zs_S, zs_T
def main(params:Params):
(feats_S, lbls_S), (feats_T, lbls_T) = create_datasets(params.source, params.target, params.num_src_classes, params.num_total_classes)
# l2 normalization and pca
feats_S, feats_T = do_l2_normalization(feats_S, feats_T)
feats_S, feats_T = do_pca(feats_S, feats_T, params.pca_dim)
feats_S, feats_T = do_l2_normalization(feats_S, feats_T)
# initial
feats_all = np.concatenate((feats_S, feats_T), axis=0)
pseudo_labels = -np.ones_like(lbls_T)
rejected = np.zeros_like(pseudo_labels)
# iterations
for t in range(1, params.T+1):
P = get_projection_matrix(feats_all, np.concatenate((lbls_S, pseudo_labels), axis=0), params.proj_dim)
proj_S, proj_T = project_features(P, feats_S), project_features(P, feats_T)
proj_S, proj_T = center_and_l2_normalize(proj_S, proj_T)
pseudo_labels, pseudo_probs = get_closed_set_pseudo_labels(proj_S, lbls_S, proj_T)
selected = select_closed_set_pseudo_labels(pseudo_labels, pseudo_probs, t, params.T)
selected = selected * (1-rejected)
if t == 2:
rejected = select_initial_rejected(pseudo_probs, params.n_r)
if t >= 2:
rejected = update_rejected(selected, rejected, proj_T)
selected = selected * (1-rejected)
pseudo_labels[selected == 0] = -1
pseudo_labels[rejected == 1] = -2
# final pseudo labels
pseudo_labels[pseudo_labels == -2] = params.num_src_classes
assert (pseudo_labels != -1).all()
# evaluation
return evaluate(pseudo_labels, lbls_T, params.num_src_classes)
if __name__ == '__main__':
params = Params(pca_dim=512, proj_dim=128, T=10, n_r=1200,
dataset='OfficeHome', source='clipart', target='art',
num_src_classes=25, num_total_classes=65)
print(params.source, params.target, main(params))