Integration of lightning_utilties function into flash

flash/core/data/base_viz.py

@@ -13,11 +13,11 @@
 # limitations under the License.
 from typing import Any, Dict, List, Set, Tuple
 
+from lightning_utilities.core.overrides import is_overridden
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 from flash.core.data.callback import BaseDataFetcher
 from flash.core.data.utils import _CALLBACK_FUNCS
-from flash.core.utilities.apply_func import _is_overridden
 from flash.core.utilities.stages import RunningStage
 
 
@@ -124,7 +124,7 @@ def show(
 
         for func_name in func_names_set:
             hook_name = f"show_{func_name}"
-            if _is_overridden(hook_name, self, BaseVisualization):
+            if is_overridden(hook_name, self, BaseVisualization):
                 getattr(self, hook_name)(batch[func_name], running_stage, limit_nb_samples, figsize)
 
     def show_load_sample(

flash/core/utilities/apply_func.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 from typing import Callable, Dict, Mapping, Sequence, Type, Union
 
+from lightning_utilities.core.overrides import is_overridden
 from torch import nn
 
 
@@ -29,13 +30,3 @@ def get_callable_dict(fn: Union[nn.Module, Callable, Mapping, Sequence]) -> Unio
         return {get_callable_name(f): f for f in fn}
     if callable(fn):
         return {get_callable_name(fn): fn}
-
-
-def _is_overridden(method_name: str, instance: object, parent: Type[object]) -> bool:
-    """Cropped Version of https://github.com/Lightning-
-    AI/lightning/blob/master/src/pytorch_lightning/utilities/model_helpers.py."""
-
-    if not hasattr(instance, method_name):
-        return False
-
-    return getattr(instance, method_name).__code__ != getattr(parent, method_name).__code__

flash/core/utilities/imports.py

@@ -16,10 +16,9 @@
 import operator
 import os
 import types
-from importlib.util import find_spec
-from typing import Callable, List, Tuple, Union
+from typing import List, Tuple, Union
 
-import pkg_resources
+from lightning_utilities.core.imports import compare_version, module_available
 from pkg_resources import DistributionNotFound
 
 try:
@@ -28,97 +27,50 @@
     Version = None
 
 
-def _module_available(module_path: str) -> bool:
-    """Check if a path is available in your environment.
-
-    >>> _module_available('os')
-    True
-    >>> _module_available('bla.bla')
-    False
-    """
-    try:
-        return find_spec(module_path) is not None
-    except AttributeError:
-        # Python 3.6
-        return False
-    except ModuleNotFoundError:
-        # Python 3.7+
-        return False
-    except ValueError:
-        # Sometimes __spec__ can be None and gives a ValueError
-        return True
-
-
-def _compare_version(package: str, op: Callable, version: str, use_base_version: bool = False) -> bool:
-    """Compare package version with some requirements.
-
-    >>> _compare_version("torch", operator.ge, "0.1")
-    True
-    >>> _compare_version("does_not_exist", operator.ge, "0.0")
-    False
-    """
-    try:
-        pkg = importlib.import_module(package)
-    except (ImportError, DistributionNotFound):
-        return False
-    try:
-        if hasattr(pkg, "__version__"):
-            pkg_version = Version(pkg.__version__)
-        else:
-            # try pkg_resources to infer version
-            pkg_version = Version(pkg_resources.get_distribution(package).version)
-    except TypeError:
-        # this is mocked by Sphinx, so it should return True to generate all summaries
-        return True
-    if use_base_version:
-        pkg_version = Version(pkg_version.base_version)
-    return op(pkg_version, Version(version))
-
-
-_TORCH_AVAILABLE = _module_available("torch")
-_PL_AVAILABLE = _module_available("pytorch_lightning")
-_BOLTS_AVAILABLE = _module_available("pl_bolts") and _compare_version("torch", operator.lt, "1.9.0")
-_PANDAS_AVAILABLE = _module_available("pandas")
-_SKLEARN_AVAILABLE = _module_available("sklearn")
-_PYTORCHTABULAR_AVAILABLE = _module_available("pytorch_tabular")
-_FORECASTING_AVAILABLE = _module_available("pytorch_forecasting")
-_KORNIA_AVAILABLE = _module_available("kornia")
-_COCO_AVAILABLE = _module_available("pycocotools")
-_TIMM_AVAILABLE = _module_available("timm")
-_TORCHVISION_AVAILABLE = _module_available("torchvision")
-_PYTORCHVIDEO_AVAILABLE = _module_available("pytorchvideo")
-_MATPLOTLIB_AVAILABLE = _module_available("matplotlib")
-_TRANSFORMERS_AVAILABLE = _module_available("transformers")
-_PYSTICHE_AVAILABLE = _module_available("pystiche")
-_FIFTYONE_AVAILABLE = _module_available("fiftyone")
-_FASTAPI_AVAILABLE = _module_available("fastapi")
-_PYDANTIC_AVAILABLE = _module_available("pydantic")
-_GRAPHVIZ_AVAILABLE = _module_available("graphviz")
-_CYTOOLZ_AVAILABLE = _module_available("cytoolz")
-_UVICORN_AVAILABLE = _module_available("uvicorn")
-_PIL_AVAILABLE = _module_available("PIL")
-_OPEN3D_AVAILABLE = _module_available("open3d")
-_SEGMENTATION_MODELS_AVAILABLE = _module_available("segmentation_models_pytorch")
-_FASTFACE_AVAILABLE = _module_available("fastface") and _compare_version("pytorch_lightning", operator.lt, "1.5.0")
-_LIBROSA_AVAILABLE = _module_available("librosa")
-_TORCH_SCATTER_AVAILABLE = _module_available("torch_scatter")
-_TORCH_SPARSE_AVAILABLE = _module_available("torch_sparse")
-_TORCH_GEOMETRIC_AVAILABLE = _module_available("torch_geometric")
-_NETWORKX_AVAILABLE = _module_available("networkx")
-_TORCHAUDIO_AVAILABLE = _module_available("torchaudio")
-_SENTENCEPIECE_AVAILABLE = _module_available("sentencepiece")
-_DATASETS_AVAILABLE = _module_available("datasets")
-_TM_TEXT_AVAILABLE: bool = _module_available("torchmetrics.text")
-_ICEVISION_AVAILABLE = _module_available("icevision")
-_ICEDATA_AVAILABLE = _module_available("icedata")
-_LEARN2LEARN_AVAILABLE = _module_available("learn2learn") and _compare_version("learn2learn", operator.ge, "0.1.6")
-_TORCH_ORT_AVAILABLE = _module_available("torch_ort")
-_VISSL_AVAILABLE = _module_available("vissl") and _module_available("classy_vision")
-_ALBUMENTATIONS_AVAILABLE = _module_available("albumentations")
-_BAAL_AVAILABLE = _module_available("baal")
-_TORCH_OPTIMIZER_AVAILABLE = _module_available("torch_optimizer")
-_SENTENCE_TRANSFORMERS_AVAILABLE = _module_available("sentence_transformers")
-_DEEPSPEED_AVAILABLE = _module_available("deepspeed")
+_TORCH_AVAILABLE = module_available("torch")
+_PL_AVAILABLE = module_available("pytorch_lightning")
+_BOLTS_AVAILABLE = module_available("pl_bolts") and compare_version("torch", operator.lt, "1.9.0")
+_PANDAS_AVAILABLE = module_available("pandas")
+_SKLEARN_AVAILABLE = module_available("sklearn")
+_PYTORCHTABULAR_AVAILABLE = module_available("pytorch_tabular")
+_FORECASTING_AVAILABLE = module_available("pytorch_forecasting")
+_KORNIA_AVAILABLE = module_available("kornia")
+_COCO_AVAILABLE = module_available("pycocotools")
+_TIMM_AVAILABLE = module_available("timm")
+_TORCHVISION_AVAILABLE = module_available("torchvision")
+_PYTORCHVIDEO_AVAILABLE = module_available("pytorchvideo")
+_MATPLOTLIB_AVAILABLE = module_available("matplotlib")
+_TRANSFORMERS_AVAILABLE = module_available("transformers")
+_PYSTICHE_AVAILABLE = module_available("pystiche")
+_FIFTYONE_AVAILABLE = module_available("fiftyone")
+_FASTAPI_AVAILABLE = module_available("fastapi")
+_PYDANTIC_AVAILABLE = module_available("pydantic")
+_GRAPHVIZ_AVAILABLE = module_available("graphviz")
+_CYTOOLZ_AVAILABLE = module_available("cytoolz")
+_UVICORN_AVAILABLE = module_available("uvicorn")
+_PIL_AVAILABLE = module_available("PIL")
+_OPEN3D_AVAILABLE = module_available("open3d")
+_SEGMENTATION_MODELS_AVAILABLE = module_available("segmentation_models_pytorch")
+_FASTFACE_AVAILABLE = module_available("fastface") and compare_version("pytorch_lightning", operator.lt, "1.5.0")
+_LIBROSA_AVAILABLE = module_available("librosa")
+_TORCH_SCATTER_AVAILABLE = module_available("torch_scatter")
+_TORCH_SPARSE_AVAILABLE = module_available("torch_sparse")
+_TORCH_GEOMETRIC_AVAILABLE = module_available("torch_geometric")
+_NETWORKX_AVAILABLE = module_available("networkx")
+_TORCHAUDIO_AVAILABLE = module_available("torchaudio")
+_SENTENCEPIECE_AVAILABLE = module_available("sentencepiece")
+_DATASETS_AVAILABLE = module_available("datasets")
+_TM_TEXT_AVAILABLE: bool = module_available("torchmetrics.text")
+_ICEVISION_AVAILABLE = module_available("icevision")
+_ICEDATA_AVAILABLE = module_available("icedata")
+_LEARN2LEARN_AVAILABLE = module_available("learn2learn") and compare_version("learn2learn", operator.ge, "0.1.6")
+_TORCH_ORT_AVAILABLE = module_available("torch_ort")
+_VISSL_AVAILABLE = module_available("vissl") and module_available("classy_vision")
+_ALBUMENTATIONS_AVAILABLE = module_available("albumentations")
+_BAAL_AVAILABLE = module_available("baal")
+_TORCH_OPTIMIZER_AVAILABLE = module_available("torch_optimizer")
+_SENTENCE_TRANSFORMERS_AVAILABLE = module_available("sentence_transformers")
+_DEEPSPEED_AVAILABLE = module_available("deepspeed")
 
 
 if _PIL_AVAILABLE:
@@ -130,15 +82,15 @@ class Image:
 
 
 if Version:
-    _TORCHVISION_GREATER_EQUAL_0_9 = _compare_version("torchvision", operator.ge, "0.9.0")
-    _PL_GREATER_EQUAL_1_4_3 = _compare_version("pytorch_lightning", operator.ge, "1.4.3")
-    _PL_GREATER_EQUAL_1_4_0 = _compare_version("pytorch_lightning", operator.ge, "1.4.0")
-    _PL_GREATER_EQUAL_1_5_0 = _compare_version("pytorch_lightning", operator.ge, "1.5.0")
-    _PL_GREATER_EQUAL_1_6_0 = _compare_version("pytorch_lightning", operator.ge, "1.6.0rc0")
-    _PANDAS_GREATER_EQUAL_1_3_0 = _compare_version("pandas", operator.ge, "1.3.0")
-    _ICEVISION_GREATER_EQUAL_0_11_0 = _compare_version("icevision", operator.ge, "0.11.0")
-    _TM_GREATER_EQUAL_0_7_0 = _compare_version("torchmetrics", operator.ge, "0.7.0")
-    _BAAL_GREATER_EQUAL_1_5_2 = _compare_version("baal", operator.ge, "1.5.2")
+    _TORCHVISION_GREATER_EQUAL_0_9 = compare_version("torchvision", operator.ge, "0.9.0")
+    _PL_GREATER_EQUAL_1_4_3 = compare_version("pytorch_lightning", operator.ge, "1.4.3")
+    _PL_GREATER_EQUAL_1_4_0 = compare_version("pytorch_lightning", operator.ge, "1.4.0")
+    _PL_GREATER_EQUAL_1_5_0 = compare_version("pytorch_lightning", operator.ge, "1.5.0")
+    _PL_GREATER_EQUAL_1_6_0 = compare_version("pytorch_lightning", operator.ge, "1.6.0rc0")
+    _PANDAS_GREATER_EQUAL_1_3_0 = compare_version("pandas", operator.ge, "1.3.0")
+    _ICEVISION_GREATER_EQUAL_0_11_0 = compare_version("icevision", operator.ge, "0.11.0")
+    _TM_GREATER_EQUAL_0_7_0 = compare_version("torchmetrics", operator.ge, "0.7.0")
+    _BAAL_GREATER_EQUAL_1_5_2 = compare_version("baal", operator.ge, "1.5.2")
 
 _TEXT_AVAILABLE = all(
     [
@@ -193,7 +145,7 @@ def decorator(func):
                         available = False
                 else:
                     modules.append(module_path)
-                    if not _module_available(module_path):
+                    if not module_available(module_path):
                         available = False
             else:
                 available, module_path = module_path

flash/image/detection/backbones.py

@@ -14,6 +14,8 @@
 from functools import partial
 from typing import Optional
 
+from lightning_utilities.core.imports import module_available
+
 from flash.core.adapter import Adapter
 from flash.core.integrations.icevision.adapter import IceVisionAdapter, SimpleCOCOMetric
 from flash.core.integrations.icevision.backbones import (
@@ -23,7 +25,7 @@
 )
 from flash.core.model import Task
 from flash.core.registry import FlashRegistry
-from flash.core.utilities.imports import _ICEVISION_AVAILABLE, _module_available, _TORCHVISION_AVAILABLE
+from flash.core.utilities.imports import _ICEVISION_AVAILABLE, _TORCHVISION_AVAILABLE
 from flash.core.utilities.providers import _EFFDET, _ICEVISION, _MMDET, _TORCHVISION, _ULTRALYTICS
 
 if _ICEVISION_AVAILABLE:
@@ -75,7 +77,7 @@ def from_task(
                 providers=[_ICEVISION, _TORCHVISION],
             )
 
-    if _module_available("yolov5"):
+    if module_available("yolov5"):
         model_type = icevision_models.ultralytics.yolov5
         OBJECT_DETECTION_HEADS(
             partial(load_icevision_with_image_size, model_type),
@@ -85,7 +87,7 @@ def from_task(
             providers=[_ICEVISION, _ULTRALYTICS],
         )
 
-    if _module_available("mmdet"):
+    if module_available("mmdet"):
         for model_type in [
             icevision_models.mmdet.faster_rcnn,
             icevision_models.mmdet.retinanet,
@@ -100,7 +102,7 @@ def from_task(
                 providers=[_ICEVISION, _MMDET],
             )
 
-    if _module_available("effdet"):
+    if module_available("effdet"):
 
         model_type = icevision_models.ross.efficientdet
         OBJECT_DETECTION_HEADS(

flash/image/instance_segmentation/backbones.py

@@ -14,12 +14,14 @@
 from functools import partial
 from typing import Optional
 
+from lightning_utilities.core.imports import module_available
+
 from flash.core.adapter import Adapter
 from flash.core.integrations.icevision.adapter import IceVisionAdapter, SimpleCOCOMetric
 from flash.core.integrations.icevision.backbones import get_backbones, load_icevision_ignore_image_size
 from flash.core.model import Task
 from flash.core.registry import FlashRegistry
-from flash.core.utilities.imports import _ICEVISION_AVAILABLE, _module_available, _TORCHVISION_AVAILABLE
+from flash.core.utilities.imports import _ICEVISION_AVAILABLE, _TORCHVISION_AVAILABLE
 from flash.core.utilities.providers import _ICEVISION, _MMDET, _TORCHVISION
 
 if _ICEVISION_AVAILABLE:
@@ -71,7 +73,7 @@ def from_task(
             providers=[_ICEVISION, _TORCHVISION],
         )
 
-    if _module_available("mmdet"):
+    if module_available("mmdet"):
         model_type = icevision_models.mmdet.mask_rcnn
         INSTANCE_SEGMENTATION_HEADS(
             partial(load_icevision_ignore_image_size, model_type),

requirements.txt

@@ -10,3 +10,4 @@ jsonargparse[signatures]>=3.17.0, <=4.9.0
 click>=7.1.2
 protobuf<=3.20.1
 fsspec
+lightning_utilities>=0.3.0