def test_feature_decoder(self):
# Make random feature.
feat_size = 64
feat = np.random.randn(feat_size).astype(np.float32)
label = np.zeros(1).astype(np.int64)
# Encode
feat_example = dataset_to_records.make_example([
('image/embedding', 'float32', feat),
('image/class/label', 'int64', [label]),
])
# Decode
feat_decoder = decoder.FeatureDecoder(feat_len=feat_size)
feat_decoded = feat_decoder(feat_example)
# Assert perfect reconstruction.
with self.session(use_gpu=False) as sess:
feat_rec_numpy = sess.run(feat_decoded)
self.assertAllEqual(feat_rec_numpy, feat)
def test_feature_decoder(self):
# Make random feature.
feat_size = 64
feat = np.random.randn(feat_size).astype(np.float32)
label = np.zeros(1).astype(np.int64)
with self.session(use_gpu=False) as sess:
feat_serial = sess.run(tf.io.serialize_tensor(feat))
# Encode
feat_example = dataset_to_records.make_example(
feat_serial,
label,
input_key='image/embedding',
label_key='image/class/label')
# Decode
feat_decoder = decoder.FeatureDecoder()
feat_decoded = feat_decoder(feat_example)
# Assert perfect reconstruction.
with self.session(use_gpu=False) as sess:
feat_rec_numpy = sess.run(feat_decoded)
self.assertAllEqual(feat_rec_numpy, feat)