Python archival.load_archive方法代碼示例

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def __init__(self,
                archive_file=DEFAULT_ARCHIVE_FILE,
                cuda_device=DEFAULT_CUDA_DEVICE,
                model_file=None):
        """ Constructor for NLU class. """
        check_for_gpu(cuda_device)

        if not os.path.isfile(archive_file):
            if not model_file:
                raise Exception("No model for JointNLU is specified!")
            archive_file = cached_path(model_file)


        archive = load_archive(archive_file,
                            cuda_device=cuda_device)
        self.tokenizer = SpacyWordSplitter(language="en_core_web_sm")
        dataset_reader_params = archive.config["dataset_reader"]
        self.dataset_reader = DatasetReader.from_params(dataset_reader_params)
        self.model = archive.model
        self.model.eval() 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def __init__(self, 
                archive_file=DEFAULT_ARCHIVE_FILE,
                cuda_device=DEFAULT_CUDA_DEVICE,
                model_file=None):
        """ Constructor for NLU class. """
        SysPolicy.__init__(self)

        check_for_gpu(cuda_device)

        if not os.path.isfile(archive_file):
            if not model_file:
                raise Exception("No model for MILU is specified!")
            archive_file = cached_path(model_file)

        archive = load_archive(archive_file,
                            cuda_device=cuda_device)
        dataset_reader_params = archive.config["dataset_reader"]
        self.dataset_reader = DatasetReader.from_params(dataset_reader_params)
        self.action_decoder = MultiWozVocabActionDecoder()
        self.action_decoder.action_vocab = self.dataset_reader.action_vocab
        self.state_encoder = self.dataset_reader.state_encoder
        self.model = archive.model
        self.model.eval() 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_archiving(self):
        # copy params, since they'll get consumed during training
        params_copy = copy.deepcopy(self.params.as_dict())

        # `train_model` should create an archive
        serialization_dir = self.TEST_DIR / "archive_test"
        model = train_model(self.params, serialization_dir=serialization_dir)

        archive_path = serialization_dir / "model.tar.gz"

        # load from the archive
        archive = load_archive(archive_path)
        model2 = archive.model

        assert_models_equal(model, model2)

        # check that params are the same
        params2 = archive.config
        assert params2.as_dict() == params_copy 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_can_load_from_archive_model(self):
        serialization_dir = self.FIXTURES_ROOT / "basic_classifier" / "from_archive_serialization"
        archive_path = serialization_dir / "model.tar.gz"
        model = load_archive(archive_path).model

        # We want to be sure that we don't just not crash, but also be sure that we loaded the right
        # weights for the model.  We'll do that by making sure that we didn't just load the model
        # that's in the `archive_path` of the config file, which is this one.
        base_model_path = self.FIXTURES_ROOT / "basic_classifier" / "serialization" / "model.tar.gz"
        base_model = load_archive(base_model_path).model
        base_model_params = dict(base_model.named_parameters())
        for name, parameters in model.named_parameters():
            if parameters.size() == base_model_params[name].size():
                assert not (parameters == base_model_params[name]).all()
            else:
                # In this case, the parameters are definitely different, no need for the above
                # check.
                pass 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_simple_gradient_basic_text(self):
        inputs = {"sentence": "It was the ending that I hated"}
        archive = load_archive(
            self.FIXTURES_ROOT / "basic_classifier" / "serialization" / "model.tar.gz"
        )
        predictor = Predictor.from_archive(archive, "text_classifier")

        interpreter = SimpleGradient(predictor)
        interpretation = interpreter.saliency_interpret_from_json(inputs)
        assert interpretation is not None
        assert "instance_1" in interpretation
        assert "grad_input_1" in interpretation["instance_1"]
        grad_input_1 = interpretation["instance_1"]["grad_input_1"]
        assert len(grad_input_1) == 7  # 7 words in input

        # two interpretations should be identical for gradient
        repeat_interpretation = interpreter.saliency_interpret_from_json(inputs)
        repeat_grad_input_1 = repeat_interpretation["instance_1"]["grad_input_1"]
        for grad, repeat_grad in zip(grad_input_1, repeat_grad_input_1):
            assert grad == approx(repeat_grad) 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_hotflip(self):
        inputs = {"sentence": "I always write unit tests for my code."}

        archive = load_archive(
            self.FIXTURES_ROOT / "basic_classifier" / "serialization" / "model.tar.gz"
        )
        predictor = Predictor.from_archive(archive)

        hotflipper = Hotflip(predictor)
        hotflipper.initialize()
        attack = hotflipper.attack_from_json(inputs, "tokens", "grad_input_1")
        assert attack is not None
        assert "final" in attack
        assert "original" in attack
        assert "outputs" in attack
        assert len(attack["final"][0]) == len(
            attack["original"]
        )  # hotflip replaces words without removing 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_integrated_gradient(self):
        inputs = {"sentence": "It was the ending that I hated"}
        archive = load_archive(
            self.FIXTURES_ROOT / "basic_classifier" / "serialization" / "model.tar.gz"
        )
        predictor = Predictor.from_archive(archive, "text_classifier")

        interpreter = IntegratedGradient(predictor)
        interpretation = interpreter.saliency_interpret_from_json(inputs)
        assert interpretation is not None
        assert "instance_1" in interpretation
        assert "grad_input_1" in interpretation["instance_1"]
        grad_input_1 = interpretation["instance_1"]["grad_input_1"]
        assert len(grad_input_1) == 7  # 7 words in input

        # two interpretations should be identical for integrated gradients
        repeat_interpretation = interpreter.saliency_interpret_from_json(inputs)
        repeat_grad_input_1 = repeat_interpretation["instance_1"]["grad_input_1"]
        for grad, repeat_grad in zip(grad_input_1, repeat_grad_input_1):
            assert grad == approx(repeat_grad) 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_transferring_of_modules_ensures_type_consistency(self):

        model_archive = str(
            self.FIXTURES_ROOT / "basic_classifier" / "serialization" / "model.tar.gz"
        )
        trained_model = load_archive(model_archive).model

        config_file = str(self.FIXTURES_ROOT / "basic_classifier" / "experiment_seq2seq.jsonnet")
        model_params = Params.from_file(config_file).pop("model").as_dict(quiet=True)

        # Override only text_field_embedder and make it load Seq2SeqEncoder
        model_params["text_field_embedder"] = {
            "_pretrained": {
                "archive_file": model_archive,
                "module_path": "_seq2seq_encoder._module",
            }
        }
        with pytest.raises(ConfigurationError):
            Model.from_params(vocab=trained_model.vocab, params=Params(model_params)) 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_loads_correct_dataset_reader(self):
        # This model has a different dataset reader configuration for train and validation. The
        # parameter that differs is the token indexer's namespace.
        archive = load_archive(
            self.FIXTURES_ROOT / "simple_tagger_with_span_f1" / "serialization" / "model.tar.gz"
        )

        predictor = Predictor.from_archive(archive, "sentence_tagger")
        assert predictor._dataset_reader._token_indexers["tokens"].namespace == "test_tokens"

        predictor = Predictor.from_archive(
            archive, "sentence_tagger", dataset_reader_to_load="train"
        )
        assert predictor._dataset_reader._token_indexers["tokens"].namespace == "tokens"

        predictor = Predictor.from_archive(
            archive, "sentence_tagger", dataset_reader_to_load="validation"
        )
        assert predictor._dataset_reader._token_indexers["tokens"].namespace == "test_tokens" 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_get_gradients(self):
        inputs = {
            "sentence": "I always write unit tests",
        }

        archive = load_archive(
            self.FIXTURES_ROOT / "basic_classifier" / "serialization" / "model.tar.gz"
        )
        predictor = Predictor.from_archive(archive)

        instance = predictor._json_to_instance(inputs)
        outputs = predictor._model.forward_on_instance(instance)
        labeled_instances = predictor.predictions_to_labeled_instances(instance, outputs)
        for instance in labeled_instances:
            grads = predictor.get_gradients([instance])[0]
            assert "grad_input_1" in grads
            assert grads["grad_input_1"] is not None
            assert len(grads["grad_input_1"][0]) == 5  # 9 words in hypothesis 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_get_gradients_when_requires_grad_is_false(self):
        inputs = {
            "sentence": "I always write unit tests",
        }

        archive = load_archive(
            self.FIXTURES_ROOT
            / "basic_classifier"
            / "embedding_with_trainable_is_false"
            / "model.tar.gz"
        )
        predictor = Predictor.from_archive(archive)

        # ensure that requires_grad is initially False on the embedding layer
        embedding_layer = util.find_embedding_layer(predictor._model)
        assert not embedding_layer.weight.requires_grad
        instance = predictor._json_to_instance(inputs)
        outputs = predictor._model.forward_on_instance(instance)
        labeled_instances = predictor.predictions_to_labeled_instances(instance, outputs)
        # ensure that gradients are always present, despite requires_grad being false on the embedding layer
        for instance in labeled_instances:
            grads = predictor.get_gradients([instance])[0]
            assert bool(grads)
        # ensure that no side effects remain
        assert not embedding_layer.weight.requires_grad 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def predict_model_with_archive(predictor: str, params: Params, archive: str,
                               input_file: str, output_file: str, batch_size: int = 1):
    cuda_device = params["trainer"]["cuda_device"]

    check_for_gpu(cuda_device)
    archive = load_archive(archive,
                           cuda_device=cuda_device)

    predictor = Predictor.from_archive(archive, predictor)

    manager = _PredictManager(predictor,
                              input_file,
                              output_file,
                              batch_size,
                              print_to_console=False,
                              has_dataset_reader=True)
    manager.run() 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_extra_files(self):

        serialization_dir = self.TEST_DIR / u'serialization'

        # Train a model
        train_model(self.params, serialization_dir=serialization_dir)

        # Archive model, and also archive the training data
        files_to_archive = {u"train_data_path": unicode(self.FIXTURES_ROOT / u'data' / u'sequence_tagging.tsv')}
        archive_model(serialization_dir=serialization_dir, files_to_archive=files_to_archive)

        archive = load_archive(serialization_dir / u'model.tar.gz')
        params = archive.config

        # The param in the data should have been replaced with a temporary path
        # (which we don't know, but we know what it ends with).
        assert params.get(u'train_data_path').endswith(u'/fta/train_data_path')

        # The validation data path should be the same though.
        assert params.get(u'validation_data_path') == unicode(self.FIXTURES_ROOT / u'data' / u'sequence_tagging.tsv') 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_initialize_weights_from_archive(self):
        original_model_parameters = self.model.named_parameters()
        original_model_weights = dict((name, parameter.data.clone().numpy())
                                  for name, parameter in original_model_parameters)
        # pylint: disable=line-too-long
        mml_model_archive_file = (self.FIXTURES_ROOT / u"semantic_parsing" / u"nlvr_direct_semantic_parser" /
                                  u"serialization" / u"model.tar.gz")
        archive = load_archive(mml_model_archive_file)
        archived_model_parameters = archive.model.named_parameters()
        self.model._initialize_weights_from_archive(archive)
        changed_model_parameters = dict(self.model.named_parameters())
        for name, archived_parameter in archived_model_parameters:
            archived_weight = archived_parameter.data.numpy()
            original_weight = original_model_weights[name]
            changed_weight = changed_model_parameters[name].data.numpy()
            # We want to make sure that the weights in the original model have indeed been changed
            # after a call to ``_initialize_weights_from_archive``.
            with self.assertRaises(AssertionError, msg="{name} has not changed"):
                assert_almost_equal(original_weight, changed_weight)
            # This also includes the sentence token embedder. Those weights will be the same
            # because the two models have the same vocabulary.
            assert_almost_equal(archived_weight, changed_weight) 

# 需要導入模塊: from allennlp.models import archival [as 別名]
# 或者: from allennlp.models.archival import load_archive [as 別名]
def test_get_vocab_index_mapping(self):
        # pylint: disable=line-too-long
        mml_model_archive_file = (self.FIXTURES_ROOT / u"semantic_parsing" / u"nlvr_direct_semantic_parser" /
                                  u"serialization" / u"model.tar.gz")
        archive = load_archive(mml_model_archive_file)
        mapping = self.model._get_vocab_index_mapping(archive.model.vocab)
        expected_mapping = [(i, i) for i in range(16)]
        assert mapping == expected_mapping

        new_vocab = Vocabulary()
        def copy_token_at_index(i):
            token = self.vocab.get_token_from_index(i, u"tokens")
            new_vocab.add_token_to_namespace(token, u"tokens")
        copy_token_at_index(5)
        copy_token_at_index(7)
        copy_token_at_index(10)
        mapping = self.model._get_vocab_index_mapping(new_vocab)
        # Mapping of indices from model vocabulary to new vocabulary. 0 and 1 are padding and unk
        # tokens.
        assert mapping == [(0, 0), (1, 1), (5, 2), (7, 3), (10, 4)]