[MNT] change torch warning into error

learnware/market/anchor/__init__.py

@@ -8,6 +8,6 @@ logger = get_module_logger("market_anchor")
 
 if not is_torch_available(verbose=False):
     AnchoredSearcher = None
-    logger.warning("AnchoredSearcher is skipped because 'torch' is not installed!")
+    logger.error("AnchoredSearcher is skipped because 'torch' is not installed!")
 else:
     from .searcher import AnchoredSearcher

learnware/market/easy/__init__.py

@@ -9,7 +9,7 @@ if not is_torch_available(verbose=False):
     EasySearcher = None
     EasySemanticChecker = None
     EasyStatChecker = None
-    logger.warning("EasySeacher and EasyChecker are skipped because 'torch' is not installed!")
+    logger.error("EasySeacher and EasyChecker are skipped because 'torch' is not installed!")
 else:
     from .searcher import EasySearcher, EasyStatSearcher, EasyFuzzSemanticSearcher, EasyExactSemanticSearcher
     from .checker import EasySemanticChecker, EasyStatChecker

learnware/reuse/__init__.py

@@ -18,13 +18,15 @@ if not is_torch_available(verbose=False):
     FeatureAugmentReuser = None
     HeteroMapAlignLearnware = None
     FeatureAlignLearnware = None
-    logger.warning(
-        "[AveragingReuser, FeatureAugmentReuser, HeteroMapAlignLearnware, FeatureAlignLearnware] is skipped due to 'torch' is not installed!"
+    JobSelectorReuser = None
+    logger.error(
+        "[AveragingReuser, FeatureAugmentReuser, HeteroMapAlignLearnware, FeatureAlignLearnware, JobSelectorReuser] is skipped due to 'torch' is not installed!"
     )
 else:
     from .averaging import AveragingReuser
     from .feature_augment import FeatureAugmentReuser
     from .hetero import HeteroMapAlignLearnware, FeatureAlignLearnware
+    from .job_selector import JobSelectorReuser
 
 if not is_lightgbm_available(verbose=False) or not is_torch_available(verbose=False):
     JobSelectorReuser = None
@@ -39,6 +41,6 @@ if not is_lightgbm_available(verbose=False) or not is_torch_available(verbose=Fa
         )
         if flag is False
     ]
-    logger.warning(f"JobSelectorReuser is skipped due to {uninstall_packages} is not installed!")
+    logger.error(f"JobSelectorReuser is skipped due to {uninstall_packages} is not installed!")
 else:
-    from .job_selector import JobSelectorReuser
+    

learnware/specification/regular/image/__init__.py

@@ -5,25 +5,8 @@ from ....logger import get_module_logger
 
 logger = get_module_logger("regular_image_spec")
 
-if (
-    not is_torchvision_available(verbose=False)
-    or not is_torch_optimizer_available(verbose=False)
-    or not is_torch_available(verbose=False)
-):
+if not is_torch_available(verbose=False):
     RKMEImageSpecification = None
-    uninstall_packages = [
-        value
-        for flag, value in zip(
-            [
-                is_torchvision_available(verbose=False),
-                is_torch_optimizer_available(verbose=False),
-                is_torch_available(verbose=False),
-            ],
-            ["torchvision", "torch-optimizer", "torch"],
-        )
-        if flag is False
-    ]
-
-    logger.warning(f"RKMEImageSpecification is skipped because {uninstall_packages} is not installed!")
+    logger.error(f"RKMEImageSpecification is skipped because 'torch' is not installed!")
 else:
     from .rkme import RKMEImageSpecification

learnware/specification/regular/image/rkme.py

@@ -13,7 +13,6 @@ import torch
 import torch_optimizer
 from torch import nn
 from torch.utils.data import TensorDataset, DataLoader
-from torchvision.transforms import Resize
 from tqdm import tqdm
 
 from . import cnn_gp
@@ -126,7 +125,11 @@ class RKMEImageSpecification(RegularStatSpecification):
                         raise ValueError(f"All values in image {i} are exceptional, e.g., NaN and Inf.")
                     img_mean = torch.nanmean(img)
                     X[i] = torch.where(is_nan, img_mean, img)
-
+        try:
+            from torchvision.transforms import Resize
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"RKMEImageSpecification is not available because 'torchvision' is not installed!")
+            
         if X.shape[2] != RKMEImageSpecification.IMAGE_WIDTH or X.shape[3] != RKMEImageSpecification.IMAGE_WIDTH:
             X = Resize((RKMEImageSpecification.IMAGE_WIDTH, RKMEImageSpecification.IMAGE_WIDTH), antialias=None)(X)
 

learnware/specification/regular/image/utils.py

@@ -1,23 +0,0 @@
-from ....logger import get_module_logger
-
-logger = get_module_logger("regular_image_spec_utils")
-
-
-def is_torch_optimizer_available(verbose=False):
-    try:
-        import torch_optimizer
-    except ModuleNotFoundError as err:
-        if verbose is True:
-            logger.warning("ModuleNotFoundError: torch_optimizer is not installed, please install torch_optimizer!")
-        return False
-    return True
-
-
-def is_torchvision_available(verbose=False):
-    try:
-        import torchvision
-    except ModuleNotFoundError as err:
-        if verbose is True:
-            logger.warning("ModuleNotFoundError: torchvision is not installed, please install torchvision!")
-        return False
-    return True

learnware/specification/regular/table/__init__.py

@@ -5,23 +5,12 @@ from ....logger import get_module_logger
 
 logger = get_module_logger("regular_table_spec")
 
-if not is_torch_available(verbose=False) or not is_fast_pytorch_kmeans_available(verbose=False):
+if not is_torch_available(verbose=False):
     RKMETableSpecification = None
     RKMEStatSpecification = None
     rkme_solve_qp = None
-    uninstall_packages = [
-        value
-        for flag, value in zip(
-            [
-                is_torch_available(verbose=False),
-                is_fast_pytorch_kmeans_available(verbose=False),
-            ],
-            ["torch", "fast_pytorch_kmeans"],
-        )
-        if flag is False
-    ]
-    logger.warning(
-        f"RKMETableSpecification, RKMEStatSpecification and rkme_solve_qp are skipped because {uninstall_packages} is not installed!"
+    logger.error(
+        f"RKMETableSpecification, RKMEStatSpecification and rkme_solve_qp are skipped because 'torch' is not installed!"
     )
 else:
     from .rkme import RKMETableSpecification, RKMEStatSpecification, rkme_solve_qp

learnware/specification/regular/text/__init__.py

@@ -6,24 +6,8 @@ from ....logger import get_module_logger
 
 logger = get_module_logger("regular_text_spec")
 
-if (
-    not is_sentence_transformers_available(verbose=False)
-    or not is_torch_available(verbose=False)
-    or not is_fast_pytorch_kmeans_available(verbose=False)
-):
+if not is_torch_available(verbose=False):
     RKMETextSpecification = None
-    uninstall_packages = [
-        value
-        for flag, value in zip(
-            [
-                is_sentence_transformers_available(verbose=False),
-                is_torch_available(verbose=False),
-                is_fast_pytorch_kmeans_available(verbose=False),
-            ],
-            ["sentence_transformers", "torch", "fast_pytorch_kmeans"],
-        )
-        if flag is False
-    ]
-    logger.warning(f"RKMETextSpecification is skipped because {uninstall_packages} is not installed!")
+    logger.warning(f"RKMETextSpecification is skipped because 'torch' is not installed!")
 else:
     from .rkme import RKMETextSpecification

learnware/specification/system/__init__.py

@@ -6,6 +6,6 @@ logger = get_module_logger("system_spec")
 
 if not is_torch_available(verbose=False):
     HeteroMapTableSpecification = None
-    logger.warning("HeteroMapTableSpecification is skipped because torch is not installed!")
+    logger.error("HeteroMapTableSpecification is skipped because torch is not installed!")
 else:
     from .hetero_table import HeteroMapTableSpecification