Add torch model import

mlem/contrib/pandas.py

@@ -31,13 +31,7 @@
 
 from mlem.config import MlemConfigBase, project_config
 from mlem.contrib.numpy import np_type_from_string, python_type_from_np_type
-from mlem.core.artifacts import (
-    Artifact,
-    Artifacts,
-    PlaceholderArtifact,
-    Storage,
-    get_file_info,
-)
+from mlem.core.artifacts import Artifact, Artifacts, Storage
 from mlem.core.data_type import (
     DataHook,
     DataReader,
@@ -50,9 +44,9 @@
     SerializationError,
     UnsupportedDataBatchLoadingType,
 )
-from mlem.core.import_objects import ExtImportHook
+from mlem.core.import_objects import ExtImportHook, LoadAndAnalyzeImportHook
 from mlem.core.meta_io import Location
-from mlem.core.objects import MlemData, MlemObject
+from mlem.core.objects import MlemData
 from mlem.core.requirements import LibRequirementsMixin
 
 _PD_EXT_TYPES = {
@@ -674,35 +668,20 @@ def write(
         }
 
 
-class PandasImport(ExtImportHook):
+class PandasImport(ExtImportHook, LoadAndAnalyzeImportHook):
     EXTS: ClassVar = tuple(f".{k}" for k in PANDAS_FORMATS)
     type: ClassVar = "pandas"
+    force_type: ClassVar = MlemData
 
     @classmethod
     def is_object_valid(cls, obj: Location) -> bool:
         return super().is_object_valid(obj) and obj.fs.isfile(obj.fullpath)
 
     @classmethod
-    def process(
-        cls,
-        obj: Location,
-        copy_data: bool = True,
-        modifier: Optional[str] = None,
-        **kwargs,
-    ) -> MlemObject:
-        ext = modifier or posixpath.splitext(obj.path)[1][1:]
+    def load_obj(cls, location: Location, modifier: Optional[str], **kwargs):
+        ext = modifier or posixpath.splitext(location.path)[1][1:]
         fmt = PANDAS_FORMATS[ext]
         read_args = fmt.read_args or {}
         read_args.update(kwargs)
-        with obj.open("rb") as f:
-            data = fmt.read_func(f, **read_args)
-        meta = MlemData.from_data(data)
-        if not copy_data:
-            meta.artifacts = {
-                DataWriter.art_name: PlaceholderArtifact(
-                    location=obj,
-                    uri=obj.uri,
-                    **get_file_info(obj.fullpath, obj.fs),
-                )
-            }
-        return meta
+        with location.open("rb") as f:
+            return fmt.read_func(f, **read_args)

mlem/contrib/torch.py

@@ -5,7 +5,7 @@
 
 from mlem.constants import PREDICT_METHOD_NAME
 from mlem.contrib.numpy import python_type_from_np_string_repr
-from mlem.core.artifacts import Artifacts, Storage
+from mlem.core.artifacts import Artifacts, FSSpecArtifact, Storage
 from mlem.core.data_type import (
     DataHook,
     DataReader,
@@ -15,7 +15,10 @@
 )
 from mlem.core.errors import DeserializationError, SerializationError
 from mlem.core.hooks import IsInstanceHookMixin
+from mlem.core.import_objects import LoadAndAnalyzeImportHook
+from mlem.core.meta_io import Location
 from mlem.core.model import ModelHook, ModelIO, ModelType, Signature
+from mlem.core.objects import MlemModel
 from mlem.core.requirements import InstallableRequirement, Requirements
 
 
@@ -190,6 +193,26 @@ def get_requirements(self) -> Requirements:
         )
 
 
+class TorchModelImport(LoadAndAnalyzeImportHook):
+    type: ClassVar = "torch"
+    force_type: ClassVar = MlemModel
+
+    @classmethod
+    def is_object_valid(cls, obj: Location) -> bool:
+        # TODO only manual import type specification for now
+        return False
+
+    @classmethod
+    def load_obj(cls, location: Location, modifier: Optional[str], **kwargs):
+        return TorchModelIO().load(
+            {
+                TorchModelIO.art_name: FSSpecArtifact(
+                    uri=location.uri, size=0, hash=""
+                )
+            }
+        )
+
+
 # Copyright 2019 Zyfra
 # Copyright 2021 Iterative
 #

mlem/core/import_objects.py

@@ -1,6 +1,6 @@
 import pickle
 from abc import ABC, abstractmethod
-from typing import ClassVar, Optional, Tuple
+from typing import ClassVar, Optional, Tuple, Union
 
 from mlem.core.artifacts import PlaceholderArtifact, get_file_info
 from mlem.core.base import MlemABC
@@ -9,7 +9,7 @@
 from mlem.core.meta_io import Location
 from mlem.core.metadata import get_object_metadata
 from mlem.core.model import ModelIO
-from mlem.core.objects import MlemObject
+from mlem.core.objects import MlemData, MlemModel, MlemObject
 
 
 class ImportHook(Hook[MlemObject], MlemABC, ABC):
@@ -63,11 +63,8 @@ def is_object_valid(cls, obj: Location) -> bool:
         return any(obj.path.endswith(e) for e in cls.EXTS)
 
 
-class PickleImportHook(ExtImportHook):
-    """Import hook for pickle files"""
-
-    EXTS: ClassVar = (".pkl", ".pickle")
-    type: ClassVar = "pickle"
+class LoadAndAnalyzeImportHook(ImportHook, ABC):
+    force_type: ClassVar[Union[MlemModel, MlemData, None]] = None
 
     @classmethod
     def process(
@@ -77,9 +74,13 @@ def process(
         modifier: Optional[str] = None,
         **kwargs,
     ) -> MlemObject:
-        with obj.open("rb") as f:
-            data = pickle.load(f)
-        meta = get_object_metadata(data, **kwargs)
+        data = cls.load_obj(obj, modifier, **kwargs)
+        if cls.force_type is None:
+            meta = get_object_metadata(data, **kwargs)
+        elif cls.force_type is MlemModel:
+            meta = MlemModel.from_obj(data, **kwargs)
+        else:
+            meta = MlemData.from_data(data, **kwargs)
         if not copy_data:
             meta.artifacts = {
                 ModelIO.art_name: PlaceholderArtifact(
@@ -89,3 +90,19 @@ def process(
                 )
             }
         return meta
+
+    @classmethod
+    def load_obj(cls, location: Location, modifier: Optional[str], **kwargs):
+        raise NotImplementedError
+
+
+class PickleImportHook(ExtImportHook, LoadAndAnalyzeImportHook):
+    """Import hook for pickle files"""
+
+    EXTS: ClassVar = (".pkl", ".pickle")
+    type: ClassVar = "pickle"
+
+    @classmethod
+    def load_obj(cls, location: Location, modifier: Optional[str], **kwargs):
+        with location.open("rb") as f:
+            return pickle.load(f)

setup.py

@@ -178,6 +178,7 @@
             "model_type.onnx = mlem.contrib.onnx:ONNXModel",
             "data_type.dataframe = mlem.contrib.pandas:DataFrameType",
             "import.pandas = mlem.contrib.pandas:PandasImport",
+            "import.torch = mlem.contrib.torch:TorchModelImport",
             "data_reader.pandas = mlem.contrib.pandas:PandasReader",
             "data_reader.pandas_series = mlem.contrib.pandas:PandasSeriesReader",
             "data_writer.pandas_series = mlem.contrib.pandas:PandasSeriesWriter",

tests/contrib/test_torch.py

@@ -3,13 +3,19 @@
 import pytest
 import torch
 
-from mlem.api import save
+from mlem.api import import_object, save
 from mlem.constants import PREDICT_METHOD_NAME
-from mlem.contrib.torch import TorchModelIO, TorchTensorReader
+from mlem.contrib.torch import (
+    TorchModel,
+    TorchModelImport,
+    TorchModelIO,
+    TorchTensorReader,
+)
 from mlem.core.artifacts import LOCAL_STORAGE
 from mlem.core.data_type import DataAnalyzer, DataType
 from mlem.core.errors import DeserializationError, SerializationError
 from mlem.core.model import ModelAnalyzer
+from mlem.core.objects import MlemModel
 from tests.conftest import data_write_read_check
 
 
@@ -153,6 +159,21 @@ def check_model(net, input_data, tmpdir):
     save(net, str(tmpdir / "torch-net"), sample_data=input_data)
 
 
+@pytest.mark.parametrize(
+    "net,torchsave",
+    [
+        (torch.nn.Linear(5, 1), torch.save),
+        (torch.jit.script(torch.nn.Linear(5, 1)), torch.jit.save),
+    ],
+)
+def test_torch_import(tmp_path, net, torchsave):
+    path = tmp_path / "model"
+    torchsave(net, path)
+    meta = import_object(str(path), type_=TorchModelImport.type)
+    assert isinstance(meta, MlemModel)
+    assert isinstance(meta.model_type, TorchModel)
+
+
 # Copyright 2019 Zyfra
 # Copyright 2021 Iterative
 #