def test_anchor_positive_triplet_mask():
"""Test function _get_anchor_positive_triplet_mask."""
num_data = 64
num_classes = 10
labels = np.random.randint(0, num_classes, size=(num_data))
mask_np = np.zeros((num_data, num_data))
for i in range(num_data):
for j in range(num_data):
distinct = (i != j)
valid = labels[i] == labels[j]
mask_np[i, j] = (distinct and valid)
mask_pt_val = _get_anchor_positive_triplet_mask(torch.as_tensor(labels))
assert np.allclose(mask_np, mask_pt_val)
def test_anchor_positive_triplet_mask():
"""Test function _get_anchor_positive_triplet_mask."""
num_data = 64
num_classes = 10
labels = np.random.randint(0, num_classes, size=(num_data)).astype(np.float32)
mask_np = np.zeros((num_data, num_data))
for i in range(num_data):
for j in range(num_data):
distinct = (i != j)
valid = labels[i] == labels[j]
mask_np[i, j] = (distinct and valid)
mask_tf = _get_anchor_positive_triplet_mask(labels)
with tf.Session() as sess:
mask_tf_val = sess.run(mask_tf)
assert np.allclose(mask_np, mask_tf_val)
#
# """Test the pairwise distances function."""
# num_data = 64
# feat_dim = 6
#
# embeddings = np.random.randn(num_data, feat_dim).astype(np.float32)
# embeddings[1] = embeddings[0] # to get distance 0
#
# with tf.Session() as sess:
# # for squared in [True, False]:
# # res_np = pairwise_distance_np(embeddings, squared=squared)
# # res_tf = sess.run(_pairwise_distances(embeddings, squared=squared))
# # assert np.allclose(res_np, res_tf)
# res_tf = sess.run(_pairwise_distances(embeddings, squared=False))
# print(res_tf.shape)
"""Test function _get_anchor_positive_triplet_mask."""
num_data = 6
num_classes = 10
labels = np.random.randint(0, num_classes, size=(num_data)).astype(np.float32)
mask_np = np.zeros((num_data, num_data))
for i in range(num_data):
for j in range(num_data):
distinct = (i != j)
valid = labels[i] == labels[j]
mask_np[i, j] = (distinct and valid)
mask_tf = _get_anchor_positive_triplet_mask(labels)
assert mask_tf.shape == [64, 64]