Multi-modal initial assumption fix (#1357)

* Multi-modal initial assumption fix
* Test for multi-modal composing added

examples/advanced/multi_modal_pipeline.py

@@ -41,6 +41,10 @@ def prepare_multi_modal_data(files_path: str, task: Task, images_size: tuple = (
     """
 
     path = os.path.join(str(fedot_project_root()), files_path)
+
+    if not os.path.exists(path):
+        raise FileNotFoundError(path)
+
     # unpacking of data archive
     unpack_archived_data(path)
     # import of table data
@@ -68,15 +72,15 @@ def prepare_multi_modal_data(files_path: str, task: Task, images_size: tuple = (
     return data
 
 
-def run_multi_modal_pipeline(files_path: str, visualization=False) -> float:
+def run_multi_modal_pipeline(files_path: str, timeout=15, visualization=False) -> float:
     task = Task(TaskTypesEnum.classification)
     images_size = (224, 224)
 
     data = prepare_multi_modal_data(files_path, task, images_size)
 
     fit_data, predict_data = train_test_data_setup(data, shuffle=True, split_ratio=0.6)
 
-    automl_model = Fedot(problem='classification', timeout=15)
+    automl_model = Fedot(problem='classification', timeout=timeout)
     pipeline = automl_model.fit(features=fit_data,
                                 target=fit_data.target)
 

fedot/api/api_utils/assumptions/assumptions_builder.py

@@ -127,6 +127,12 @@ def to_builders(self, initial_node: Optional[PipelineNode] = None,
             data_pipeline_alternatives = subbuilder.build(first_node, use_input_preprocessing=use_input_preprocessing)
             subpipelines.append(data_pipeline_alternatives)
 
+        # TODO: fix this workaround during the improvement of multi-modality
+        for i, subpipeline in enumerate(subpipelines):
+            if (len(subpipeline) == 1 and len(subpipeline[0].nodes) == 1 and
+                    str(subpipeline[0].nodes[0]) in ['cnn', 'data_source_img']):
+                subpipelines[i] = [Pipeline(PipelineNode('cnn', nodes_from=[PipelineNode('data_source_img')]))]
+
         # Then zip these alternatives together and add final node to get ensembles.
         ensemble_builders: List[PipelineBuilder] = []
         for pre_ensemble in zip(*subpipelines):

fedot/core/pipelines/tuning/tuner_builder.py

@@ -1,7 +1,10 @@
 from datetime import timedelta
 from typing import Iterable, Sequence, Type, Union
 
-from golem.core.tuning.iopt_tuner import IOptTuner
+try:
+    from golem.core.tuning.iopt_tuner import IOptTuner
+except ModuleNotFoundError:
+    print('IOpt tuner not installed')
 from golem.core.tuning.optuna_tuner import OptunaTuner
 from golem.core.tuning.simultaneous import SimultaneousTuner
 from golem.core.tuning.tuner_interface import BaseTuner