added from_tensor method

shared/dto/data_model.py

@@ -1,10 +1,9 @@
 """Definition of DataModel base class."""
-from __future__ import annotations
 
 import random
 
-from pydantic import BaseModel
-from pydantic import ValidationError
+from pydantic import BaseModel, ValidationError
+from torch import Tensor
 
 
 class DataModel(BaseModel):
@@ -33,26 +32,27 @@ class DataModel(BaseModel):
             raise ValidationError()
         assert isinstance(option, str), 'option is not a string'
         allowed_options_list = cls.list_fields()
-        assert option in allowed_options_list, \
-            f"{option} is not among allowed fields {allowed_options_list}"
+        assert (
+            option in allowed_options_list
+        ), f"{option} is not among allowed fields {allowed_options_list}"
         kwargs = {field: 0 for field in cls.list_fields()}
         kwargs[option] = 1
         return cls(**kwargs)
 
     @classmethod
-    def from_list(cls, data_list: list[float]):
+    def from_tensor(cls, tensor: Tensor):
         """Instantiate from list of values."""
-        kwargs = {key: val for key, val in zip(cls.list_fields(), data_list)}
+        assert tensor.size(dim=0) == 1, f'tensor batch larger than 1: {tensor}'
+        data_list = [float(t.item()) for t in tensor[0]]
+        kwargs = dict(zip(cls.list_fields(), data_list))
         return cls(**kwargs)
 
     def __repr__(self) -> str:
         model_dict = self.model_dump()
-        model_repr_str = f"{self.classname()}("
-        model_repr_str += ', '.join([
-            f"{field}={value:.3f}"
-            for field, value
-            in model_dict.items()
-        ])
+        model_repr_str = f'{self.classname()}('
+        model_repr_str += ', '.join(
+            [f'{field}={value:.3f}' for field, value in model_dict.items()],
+        )
         model_repr_str += ')'
         return model_repr_str