def test_serve(): model = TextClassifier(2, TEST_BACKBONE) # TODO: Currently only servable once a preprocess and postprocess have been attached model._preprocess = TextClassificationPreprocess(backbone=TEST_BACKBONE) model._postprocess = TextClassificationPostprocess() model.eval() model.serve()
def test_jit(tmpdir): sample_input = {"input_ids": torch.randint(1000, size=(1, 100))} path = os.path.join(tmpdir, "test.pt") model = TextClassifier(2, TEST_BACKBONE) model.eval() # Huggingface bert model only supports `torch.jit.trace` with `strict=False` model = torch.jit.trace(model, sample_input, strict=False) torch.jit.save(model, path) model = torch.jit.load(path) out = model(sample_input)["logits"] assert isinstance(out, torch.Tensor) assert out.shape == torch.Size([1, 2])
def test_serve(): model = TextClassifier(2, backbone=TEST_BACKBONE) model.eval() model.serve()