def test_givenABPEmbSeq2SeqModel_whenForwardPassWithTarget_thenProperlyDoPAss(
self,
load_state_dict_mock,
torch_mock,
isfile_mock,
last_version_mock,
download_weights_mock,
decoder_mock,
encoder_mock,
random_mock,
):
random_mock.return_value = self.a_value_lower_than_threshold
target_mock = MagicMock()
to_predict_mock, lengths_tensor_mock = self.setup_encoder_mocks()
# 1) We reset it later
_, decoder_hidden_mock = self.setUp_decoder_mocks(
decoder_mock, attention_mechanism=False)
decomposition_lengths_mock = MagicMock()
# We don't use the one of the setUp_decoder_mocks since we do the full loop
decoder_input_mock = MagicMock()
to_mock = MagicMock()
torch_mock.zeros().to().new_full.return_value = to_mock
# We mock the return of the decoder output
encoder_mock.__call__().return_value = (decoder_input_mock,
decoder_hidden_mock)
with patch("deepparse.network.bpemb_seq2seq.EmbeddingNetwork"
) as embedding_network_patch:
# we mock the output of the embedding layer
embedded_output_mock = MagicMock()
embedding_network_patch().return_value = embedded_output_mock
seq2seq_model = BPEmbSeq2SeqModel(self.a_torch_device,
self.output_size, self.verbose)
seq2seq_model.forward(
to_predict=to_predict_mock,
decomposition_lengths=decomposition_lengths_mock,
lengths_tensor=lengths_tensor_mock,
target=target_mock,
)
embedding_network_patch.assert_has_calls(
[call()(to_predict_mock, decomposition_lengths_mock)])
encoder_mock.assert_has_calls(
[call()(embedded_output_mock, lengths_tensor_mock)])
lengths_tensor_mock.assert_has_calls([call.max().item()])
decoder_mock.assert_has_calls([
call()(
to_mock,
decoder_hidden_mock,
decoder_input_mock,
lengths_tensor_mock,
)
])
target_mock.assert_has_calls([call.transpose(0, 1)])
def test_givenAFasttext2SeqAttModel_whenForwardPassWithTarget_thenProperlyDoPAss(
self,
load_state_dict_mock,
torch_mock,
isfile_mock,
last_version_mock,
download_weights_mock,
decoder_mock,
encoder_mock,
random_mock,
):
random_mock.return_value = self.a_value_lower_than_threshold
target_mock = MagicMock()
to_predict_mock, lengths_tensor_mock = self.setup_encoder_mocks()
_, decoder_hidden_mock = self.setUp_decoder_mocks(
decoder_mock, attention_mechanism=True)
# we don't use the one of the setUp_decoder_mocks since we do the full loop
decoder_input_mock = MagicMock()
to_mock = MagicMock()
torch_mock.zeros().to().new_full.return_value = to_mock
# We mock the return of the decoder output
encoder_mock.__call__().return_value = (decoder_input_mock,
decoder_hidden_mock)
seq2seq_model = FastTextSeq2SeqModel(
self.a_torch_device,
self.output_size,
self.verbose,
attention_mechanism=True,
)
seq2seq_model.forward(
to_predict=to_predict_mock,
lengths_tensor=lengths_tensor_mock,
target=target_mock,
)
encoder_mock.assert_has_calls(
[call()(to_predict_mock, lengths_tensor_mock)])
lengths_tensor_mock.assert_has_calls([call.max().item()])
decoder_mock.assert_has_calls([
call()(
to_mock,
decoder_hidden_mock,
decoder_input_mock,
lengths_tensor_mock,
)
])
target_mock.assert_has_calls([call.transpose(0, 1)])