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ESZSL.py
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ESZSL.py
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import numpy as np
import scipy.io
from sklearn.metrics import confusion_matrix, accuracy_score
import argparse
import pickle
parser = argparse.ArgumentParser(description='ESZSL')
parser.add_argument('--dataset', type=str, default='AWA2',
help='Name of the dataset')
parser.add_argument('--dataset_path', type=str, default='./datasets/',
help='Name of the dataset')
parser.add_argument('--filename', type=str, default='res101.mat',
help='Name of the dataset file')
parser.add_argument('--alpha', type=int, default=2,
help='value of hyper-parameter')
parser.add_argument('--gamma', type=int, default=2,
help='value of hyper-parameter')
parser.add_argument('--att_split', type=str, default='',
help='In case of LAD dataset.')
def encode_labels(Y):
i = 0
for labels in np.unique(Y):
Y[Y == labels] = i
i += 1
return Y
def compute_gzsl_accuracy(X, Y, S, weights, dataset, split, seen):
outputs = np.matmul(np.matmul(X.transpose(), weights), S)
preds = np.argmax(outputs, axis=1) + 1
if dataset == "LAD":
if seen:
np.savetxt("preds_seen_ESZSL_att"+str(split)+".txt", preds)
else:
np.savetxt("preds_unseen_ESZSL_att"+str(split)+".txt", preds)
# Compute accuracy
unique_labels = np.unique(Y)
acc = 0
for l in unique_labels:
idx = np.nonzero(Y == l)[0]
acc += accuracy_score(Y[idx], preds[idx])
acc = acc / unique_labels.shape[0]
return acc
class ESZSL:
def __init__(self, args):
self.dataset = args.dataset
self.att_split = args.att_split
res101 = scipy.io.loadmat(args.dataset_path + args.dataset + '/' + args.filename + '.mat')
att_splits = scipy.io.loadmat(args.dataset_path + args.dataset + '/att_splits' + args.att_split + '.mat')
trainval_loc = 'trainval_loc'
train_loc = 'train_loc'
val_loc = 'val_loc'
test_loc = 'test_unseen_loc'
test_seen_loc = 'test_seen_loc'
features = res101['features']
labels = res101['labels']
attributes = att_splits['att']
# Train
self.X_train = features[:, np.squeeze(att_splits[train_loc] - 1)] # shape (features_dim, n_samples)
self.Y_train = labels[np.squeeze(att_splits[train_loc] - 1)] # shape (n_samples, 1)
self.Y_train_unique = np.unique(self.Y_train) # shape (n_train_classes,)
self.S_train = attributes[:, self.Y_train_unique - 1] # shape (attributes_dim, n_train_classes)
# Validation
self.X_val = features[:, np.squeeze(att_splits[val_loc] - 1)]
self.Y_val = labels[np.squeeze(att_splits[val_loc] - 1)]
self.Y_val_unique = np.unique(self.Y_val)
self.S_val = attributes[:, self.Y_val_unique - 1]
# TrainVal
self.X_trainval = features[:, np.squeeze(att_splits[trainval_loc] - 1)]
self.Y_trainval = labels[np.squeeze(att_splits[trainval_loc] - 1)]
self.Y_trainval_unique = np.unique(self.Y_trainval)
self.S_trainval = attributes[:, self.Y_trainval_unique - 1]
# Test Unseen
self.X_test_unseen = features[:, np.squeeze(att_splits[test_loc] - 1)]
self.Y_test_unseen = labels[np.squeeze(att_splits[test_loc] - 1)]
self.Y_test_unseen_unique = np.unique(self.Y_test_unseen)
self.Y_test_unseen_orig = self.Y_test_unseen.copy()
self.S_test_unseen = attributes[:, self.Y_test_unseen_unique - 1]
# Test Seen
self.X_test_seen = features[:, np.squeeze(att_splits[test_seen_loc] - 1)]
self.Y_test_seen = labels[np.squeeze(att_splits[test_seen_loc] - 1)]
self.Y_test_seen_unique = np.unique(self.Y_test_seen)
self.Y_test_seen_orig = self.Y_test_seen.copy()
self.S_test_seen = attributes[:, self.Y_test_seen_unique - 1]
# GZSL
self.X_gzsl = np.concatenate((self.X_test_unseen, self.X_test_seen), axis=1)
self.Y_gzsl = np.concatenate((self.Y_test_unseen, self.Y_test_seen), axis=0)
self.Y_gzsl_unique = np.unique(self.Y_gzsl)
self.Y_gzsl_orig = self.Y_gzsl.copy()
self.S_gszl = attributes[:, self.Y_gzsl_unique - 1]
# Additional
self.Y_train = encode_labels(self.Y_train)
self.Y_val = encode_labels(self.Y_val)
self.Y_trainval = encode_labels(self.Y_trainval)
self.Y_test_unseen = encode_labels(self.Y_test_unseen)
self.Y_gzsl = encode_labels(self.Y_gzsl)
# params for train and val set
m_train = self.Y_train.shape[0]
z_train = len(self.Y_train_unique)
# params for trainval and test set
m_trainval = self.Y_trainval.shape[0]
z_trainval = len(self.Y_trainval_unique)
# ground truth for train and val set
self.gt_train = 0 * np.ones((m_train, z_train))
self.gt_train[np.arange(m_train), np.squeeze(self.Y_train)] = 1
# grountruth for trainval and test set
self.gt_trainval = 0 * np.ones((m_trainval, z_trainval))
self.gt_trainval[np.arange(m_trainval), np.squeeze(self.Y_trainval)] = 1
def find_hyperparams(self):
# train set
d_train = self.X_train.shape[0]
a_train = self.S_train.shape[0]
accu = 0.10
alph1 = 4
gamm1 = 1
# Weights
V = np.zeros((d_train, a_train))
for alpha in range(-3, 4):
for gamma in range(-3, 4):
# One line solution
part_1 = np.linalg.pinv(
np.matmul(self.X_train, self.X_train.transpose()) + (10 ** alpha) * np.eye(d_train))
part_0 = np.matmul(np.matmul(self.X_train, self.gt_train), self.S_train.transpose())
part_2 = np.linalg.pinv(
np.matmul(self.S_train, self.S_train.transpose()) + (10 ** gamma) * np.eye(a_train))
V = np.matmul(np.matmul(part_1, part_0), part_2)
# print(V)
# predictions
outputs = np.matmul(np.matmul(self.X_val.transpose(), V), self.S_val)
preds = np.array([np.argmax(output) for output in outputs])
# print(accuracy_score(labels_val,preds))
cm = confusion_matrix(self.Y_val, preds)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
avg = sum(cm.diagonal()) / len(self.Y_val_unique)
if avg > accu:
accu = avg
alph1 = alpha
gamm1 = gamma
print(alph1, gamm1, avg)
print("Alpha and gamma:", alph1, gamm1)
return alph1, gamm1
def train(self, alpha, gamma):
# trainval set
d_trainval = self.X_trainval.shape[0]
a_trainval = self.S_trainval.shape[0]
W = np.zeros((d_trainval, a_trainval))
part_1_test = np.linalg.pinv(
np.matmul(self.X_trainval, self.X_trainval.transpose()) + (10 ** alpha) * np.eye(d_trainval))
print(part_1_test.shape)
part_0_test = np.matmul(np.matmul(self.X_trainval, self.gt_trainval), self.S_trainval.transpose())
print(part_0_test.shape)
part_2_test = np.linalg.pinv(
np.matmul(self.S_trainval, self.S_trainval.transpose()) + (10 ** gamma) * np.eye(a_trainval))
print(part_2_test.shape)
W = np.matmul(np.matmul(part_1_test, part_0_test), part_2_test)
return W
def zsl_accuracy(self, weights):
outputs_1 = np.matmul(np.matmul(self.X_test_unseen.transpose(), weights), self.S_test_unseen)
preds_1 = np.argmax(outputs_1, axis=1)
if self.dataset == "LAD":
np.savetxt("preds_ESZSL_ZSL_att"+str(self.att_split)+".txt", preds_1)
cmat = confusion_matrix(self.Y_test_unseen, preds_1)
per_class_acc = cmat.diagonal() / cmat.sum(axis=1)
acc = np.mean(per_class_acc)
return acc
def test(self, weights):
"""
:param weights:
:return: ZSL Accuracy, GZSL Seen Accuracy, GZSL Unseen Accuracy, GZSL Harmonic Mean
"""
# ZSL
zsl_acc = self.zsl_accuracy(weights)
# GZSL
acc_seen = compute_gzsl_accuracy(self.X_test_seen, self.Y_test_seen_orig, self.S_gszl, weights, self.dataset, self.att_split, seen=True)
acc_unseen = compute_gzsl_accuracy(self.X_test_unseen, self.Y_test_unseen_orig, self.S_gszl, weights, self.dataset, self.att_split, seen=False)
harmonic_mean = (2 * acc_seen * acc_unseen) / (acc_seen + acc_unseen)
print(f"[ZSL] Top-1 Accuracy (%): {(zsl_acc * 100):.2f} %")
print(f"[GZSL]: Accuracy(%) - Seen: {(acc_seen * 100):.2f} %, Unseen: {(acc_unseen * 100):.2f} %, Harmonic: {(harmonic_mean * 100):.2f} %")
return zsl_acc, acc_seen, acc_unseen, harmonic_mean
if __name__ == '__main__':
args = parser.parse_args()
alpha = args.alpha
gamma = args.gamma
model = ESZSL(args=args)
if not args.alpha and args.gamma:
alpha, gamma = model.find_hyperparams()
weights = model.train(alpha, gamma)
zsl_acc, acc_seen, acc_unseen, harmonic_mean = model.test(weights)