def _prepare_buffer(self, file_idx: int) -> None:
assert file_idx < len(self._buffered_files_paths)
self._cur_buffered_path_context = BufferedPathContext.load(
self._buffered_files_paths[file_idx])
self._order = numpy.arange(len(self._cur_buffered_path_context))
if self.shuffle:
self._order = numpy.random.permutation(self._order)
self._cur_sample_idx = 0
def test_forward(self):
config = EncoderConfig(self._hidden_size, self._hidden_size, True, 0.5, 1, 0.5)
buffered_path_contexts = BufferedPathContext.load(self._test_data_path)
batch = PathContextBatch([buffered_path_contexts[i] for i in range(self._batch_size)])
token_vocab_size = max(batch.context[FROM_TOKEN].max().item(), batch.context[TO_TOKEN].max().item())
type_vocab_size = batch.context[PATH_TYPES].max().item()
model = PathEncoder(config, self._hidden_size, token_vocab_size + 1, 0, type_vocab_size + 1, 0)
out = model(batch.context)
number_of_paths = sum(batch.contexts_per_label)
self.assertTupleEqual((number_of_paths, self._hidden_size), out.shape)
def test_forward(self):
config = DecoderConfig(self._hidden_size, self._hidden_size, 1, 0.5, 1)
model = PathDecoder(config, self._out_size, 0, 0)
buffered_path_contexts = BufferedPathContext.load(self._test_data_path)
batch = PathContextBatch([
buffered_path_contexts[i]
for i in range(len(buffered_path_contexts))
])
number_of_paths = sum(batch.contexts_per_label)
fake_encoder_input = torch.rand(number_of_paths, self._hidden_size)
output = model(fake_encoder_input, batch.contexts_per_label,
self._target_length)
self.assertTupleEqual(
(self._target_length, len(
batch.contexts_per_label), self._out_size), output.shape)