Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
93.6k
FLAGS.data_dir, "latent_caps", "saved_train_wo_part_label.h5"))
test_data = h5py.File(path.join(
FLAGS.data_dir, "latent_caps", "saved_test_wo_part_label.h5"))
train_feat = train_data["data"][:]
train_gt = train_data["cls_label"][:]
test_feat = test_data["data"][:]
test_gt = test_data["cls_label"][:]
train_feat = train_feat.reshape([train_feat.shape[0], -1])
test_feat = test_feat.reshape([test_feat.shape[0], -1])
return train_feat, train_gt, test_feat, test_gt
def normalize(kpts):
max_bound = kpts.max(axis=1, keepdims=True)
min_bound = kpts.min(axis=1, keepdims=True)
center = (max_bound + min_bound) * 0.5
kpts -= center
max_bound = kpts.max(axis=(1, 2), keepdims=True)
min_bound = kpts.min(axis=(1, 2), keepdims=True)
scale = max_bound - min_bound
kpts /= np.maximum(scale, 1e-7)
return kpts
def load_pointnet_features():
with h5py.File(path.join(FLAGS.data_dir, "feat_valid.h5"), "r") as f:
test_feat = f["feat"][:]
test_gt = f["label"][:]
if FLAGS.feature_type == "caca" and FLAGS.use_kpts:
test_kpts = np.transpose(f["kps"][:], [0, 2, 1])
test_kpts = normalize(test_kpts)
with h5py.File(path.join(FLAGS.data_dir, "feat_train.h5"), "r") as f:
train_feat = f["feat"][:]
train_gt = f["label"][:]
if FLAGS.feature_type == "caca" and FLAGS.use_kpts:
train_kpts = np.transpose(f["kps"][:], [0, 2, 1])
train_kpts = normalize(train_kpts)
train_gt = train_gt.reshape([-1]).astype(np.uint8)
test_gt = test_gt.reshape([-1]).astype(np.uint8)
train_feat = train_feat.reshape([train_feat.shape[0], -1])
test_feat = test_feat.reshape([test_feat.shape[0], -1])
if FLAGS.feature_type == "caca" and FLAGS.use_kpts:
train_kpts = train_kpts.reshape([train_kpts.shape[0], -1])
train_feat = np.concatenate([train_feat, train_kpts], axis=-1)
test_kpts = test_kpts.reshape([test_kpts.shape[0], -1])
test_feat = np.concatenate([test_feat, test_kpts], axis=-1)
return train_feat, train_gt, test_feat, test_gt
def linear_svm_classification(train_feat, train_gt, test_feat, test_gt):
classifier = LinearSVC(verbose=1, C=0.1)
classifier.fit(train_feat, train_gt.astype(int))
return classifier.score(test_feat, test_gt.astype(int))
def construct_cost_matrix(preds, gts, n_pred_cls=13, n_gt_cls=13):
cost = np.zeros([n_pred_cls, n_gt_cls], dtype=np.int32)
for pred, gt in zip(preds, gts):
cost[pred, gt] += 1
return cost
def reassign_labels(preds, assignment):
return assignment[preds]
def equal_kmeans_classification(train_feat, train_gt, test_feat, test_gt):
cluster = KMeans(n_clusters=13, verbose=1)
cluster.fit(train_feat)
train_preds = cluster.labels_
test_preds = cluster.predict(test_feat)
c = construct_cost_matrix(train_preds, train_gt)
row_ind, col_ind = linear_sum_assignment(c, maximize=True)
test_preds = reassign_labels(test_preds, col_ind)
return (test_preds == test_gt).sum() * 1. / test_preds.shape[0]
def main(unused_args):
if FLAGS.data_dir is None:
raise ValueError("data_dir needs to be specified, {} given.".format(
FLAGS.data_dir
))
if FLAGS.feature_type == "3d_pointcaps_net":
train_feat, train_gt, test_feat, test_gt = load_3d_pointcaps_net_features()
elif FLAGS.feature_type == "pointnet" or FLAGS.feature_type == "caca":
train_feat, train_gt, test_feat, test_gt = load_pointnet_features()
if FLAGS.method_type == "svm":
accuracy = linear_svm_classification(
train_feat, train_gt, test_feat, test_gt)
elif FLAGS.method_type == "equal_kmeans":
accuracy = equal_kmeans_classification(
train_feat, train_gt, test_feat, test_gt)
print("{} feature on {}: {}".format(
FLAGS.feature_type, FLAGS.method_type, accuracy))
if __name__ == '__main__':