@@ -0,0 +1,3299 @@
+%22%22%22 Test for creation and running torch models. %22%22%22%0A# pylint: disable=import-error, no-name-in-module%0A# pylint: disable=redefined-outer-name%0Aimport pytest%0Aimport numpy as np%0A%0Afrom batchflow import Pipeline, ImagesBatch, Dataset%0Afrom batchflow import B, V, C%0Afrom batchflow.models.torch import VGG7%0A%0AMODELS = %5BVGG7%5D%0A%0A@pytest.fixture()%0Adef model_setup():%0A    %22%22%22 Pytest fixture to generate fake dataset and model config.%0A%0A    Returns%0A    -------%0A    tuple%0A        an instance of Dataset%0A        a model config%0A    %22%22%22%0A    def _model_setup():%0A        size = 50%0A        image_shape = (2, 100, 100)%0A        batch_shape = (size, *image_shape)%0A        images_array = np.random.random(batch_shape)%0A        labels_array = np.random.choice(10, size=size)%0A        data = images_array, labels_array%0A        dataset = Dataset(index=size,%0A                          batch_class=ImagesBatch,%0A                          preloaded=data)%0A%0A        model_config = %7B'inputs': %7B'images': %7B'shape': image_shape%7D,%0A                                   'labels': %7B'classes': 10%7D%7D,%0A                        'initial_block/inputs': 'images'%7D%0A        return dataset, model_config%0A%0A    return _model_setup%0A%0A@pytest.fixture()%0Adef pipeline():%0A    %22%22%22 Creates a pipeline configured to use a given model with a specified configuration.%0A%0A    Notes%0A    -----%0A    Pipeline can be executed only if its config contains the following parameters:%0A%0A    model_class : TorchModel%0A        Architecture of model. List of available models is defined at 'AVAILABLE_MODELS'.%0A%0A    model_config : Config%0A       Model parameters.%0A%0A    Returns%0A    -------%0A    Pipeline%0A        A pipeline that contains model initialization and training with a given config.%0A    %22%22%22%0A%0A    test_pipeline = (Pipeline()%0A                     .init_variable('current_loss')%0A                     .init_model('dynamic', C('model_class'),%0A                                 'model', C('model_config'))%0A                     .to_array(channels='first', dtype='float32')%0A                     .train_model('model',%0A                                  B('images'),%0A                                  B('labels'),%0A                                  fetches='loss',%0A                                  save_to=V('current_loss'))%0A                     )%0A    return test_pipeline%0A%0A@pytest.mark.slow%0A@pytest.mark.parametrize('model', MODELS)%0Aclass Test_models:%0A    %22%22%22 Ensure that a model can be built and trained.%0A%0A    There is a following pattern in every test:%0A        First of all, we get 'data' and 'config' via 'model_setup' fixture.%0A        Then we optionally modify 'config'. In this case we modify only 'model' argument.%0A        Finally, we assert that our modification was actually applied to a model by attempting%0A        to build and train it with a small batch.%0A    %22%22%22%0A    @pytest.mark.parametrize('decay', %5BNone, 'exp'%5D)%0A    def test_data_format(self, model, model_setup, pipeline, decay):%0A        %22%22%22 We can explicitly pass 'data_format' to inputs or common.%22%22%22%0A        dataset, model_config = model_setup()%0A        model_config.update(decay=decay, n_iters=25)%0A        config = %7B'model_class': model, 'model_config': model_config%7D%0A        test_pipeline = (pipeline %3C%3C dataset).set_config(config)%0A        batch = test_pipeline.next_batch(2, n_epochs=None)%0A%0A        assert len(batch) == 2%0A

add test for torch model creation