Fix import bug and refactor model registry (#12)

* Fix import error

* Rename `list_models` and add predicitons to features

Author: james77777778

README.md

@@ -24,7 +24,7 @@ import kimm
 print(kimm.list_models())
 
 # Specify the name and other arguments to filter the result
-print(kimm.list_models("efficientnet", has_pretrained=True))  # fuzzy search
+print(kimm.list_models("efficientnet", weights="imagenet"))  # fuzzy search
 
 # Initialize the model with pretrained weights
 model = kimm.models.EfficientNetV2B0(weights="imagenet")
@@ -36,7 +36,7 @@ print(y.shape)
 
 # Initialize the model as a feature extractor with pretrained weights
 model = kimm.models.EfficientNetV2B0(
-    as_feature_extractor=True, weights="imagenet"
+    feature_extractor=True, weights="imagenet"
 )
 
 # Extract features for downstream tasks

kimm/models/__init__.py

@@ -1,7 +1,10 @@
 from kimm.models.base_model import BaseModel
+from kimm.models.densenet import *  # noqa:F403
 from kimm.models.efficientnet import *  # noqa:F403
 from kimm.models.ghostnet import *  # noqa:F403
+from kimm.models.inception_v3 import *  # noqa:F403
 from kimm.models.mobilenet_v2 import *  # noqa:F403
 from kimm.models.mobilenet_v3 import *  # noqa:F403
+from kimm.models.mobilevit import *  # noqa:F403
 from kimm.models.resnet import *  # noqa:F403
 from kimm.models.vision_transformer import *  # noqa:F403

kimm/models/base_model.py

@@ -17,13 +17,13 @@ def __init__(
         feature_keys: typing.Optional[typing.List[str]] = None,
         **kwargs,
     ):
-        self.as_feature_extractor = kwargs.pop("as_feature_extractor", False)
+        self.feature_extractor = kwargs.pop("feature_extractor", False)
         self.feature_keys = feature_keys
-        if self.as_feature_extractor:
+        if self.feature_extractor:
             if features is None:
                 raise ValueError(
                     "`features` must be set when "
-                    f"`as_feature_extractor=True`. Got features={features}"
+                    f"`feature_extractor=True`. Received features={features}"
                 )
             if self.feature_keys is None:
                 self.feature_keys = list(features.keys())
@@ -35,6 +35,9 @@ def __init__(
                         f"are: {list(features.keys())}"
                     )
                 filtered_features[k] = features[k]
+            # add outputs
+            if backend.is_keras_tensor(outputs):
+                filtered_features["TOP"] = outputs
             super().__init__(inputs=inputs, outputs=filtered_features, **kwargs)
         else:
             del features
@@ -139,7 +142,7 @@ def get_config(self):
             "name": self.name,
             "trainable": self.trainable,
             # feature extractor
-            "as_feature_extractor": self.as_feature_extractor,
+            "feature_extractor": self.feature_extractor,
             "feature_keys": self.feature_keys,
             # common
             "input_shape": self.input_shape[1:],

kimm/models/base_model_test.py

@@ -39,31 +39,31 @@ def test_feature_extractor(self):
         x = random.uniform([1, 224, 224, 3])
 
         # availiable_feature_keys
-        self.assertEqual(
-            SampleModel.available_feature_keys(),
+        self.assertContainsSubset(
             ["S2", "S4", "S8", "S16", "S32"],
+            SampleModel.available_feature_keys(),
         )
 
-        # as_feature_extractor=False
+        # feature_extractor=False
         model = SampleModel()
 
         y = model(x, training=False)
 
         self.assertNotIsInstance(y, dict)
         self.assertEqual(list(y.shape), [1, 7, 7, 3])
 
-        # as_feature_extractor=True
-        model = SampleModel(as_feature_extractor=True)
+        # feature_extractor=True
+        model = SampleModel(feature_extractor=True)
 
         y = model(x, training=False)
 
         self.assertIsInstance(y, dict)
         self.assertEqual(list(y["S2"].shape), [1, 112, 112, 3])
         self.assertEqual(list(y["S32"].shape), [1, 7, 7, 3])
 
-        # as_feature_extractor=True with feature_keys
+        # feature_extractor=True with feature_keys
         model = SampleModel(
-            as_feature_extractor=True, feature_keys=["S2", "S16", "S32"]
+            feature_extractor=True, feature_keys=["S2", "S16", "S32"]
         )
 
         y = model(x, training=False)

kimm/models/densenet.py

@@ -315,7 +315,7 @@ def __init__(
         )
 
 
-add_model_to_registry(DenseNet121, True)
-add_model_to_registry(DenseNet161, True)
-add_model_to_registry(DenseNet169, True)
-add_model_to_registry(DenseNet201, True)
+add_model_to_registry(DenseNet121, "imagenet")
+add_model_to_registry(DenseNet161, "imagenet")
+add_model_to_registry(DenseNet169, "imagenet")
+add_model_to_registry(DenseNet201, "imagenet")

kimm/models/densenet_test.py

@@ -21,15 +21,14 @@ def test_densenet_base(self, model_class):
     @parameterized.named_parameters([(DenseNet121.__name__, DenseNet121)])
     def test_densenet_feature_extractor(self, model_class):
         x = random.uniform([1, 224, 224, 3]) * 255.0
-        model = model_class(
-            input_shape=[224, 224, 3], as_feature_extractor=True
-        )
+        model = model_class(input_shape=[224, 224, 3], feature_extractor=True)
 
         y = model(x, training=False)
 
         self.assertIsInstance(y, dict)
-        self.assertAllEqual(
-            list(y.keys()), model_class.available_feature_keys()
+        self.assertContainsSubset(
+            model_class.available_feature_keys(),
+            list(y.keys()),
         )
         self.assertEqual(list(y["STEM_S4"].shape), [1, 56, 56, 64])
         self.assertEqual(list(y["BLOCK0_S8"].shape), [1, 28, 28, 128])

kimm/models/efficientnet.py

@@ -1514,29 +1514,29 @@ def __init__(
         )
 
 
-add_model_to_registry(EfficientNetB0, True)
-add_model_to_registry(EfficientNetB1, True)
-add_model_to_registry(EfficientNetB2, True)
-add_model_to_registry(EfficientNetB3, True)
-add_model_to_registry(EfficientNetB4, True)
-add_model_to_registry(EfficientNetB5, True)
-add_model_to_registry(EfficientNetB6, True)
-add_model_to_registry(EfficientNetB7, True)
-add_model_to_registry(EfficientNetLiteB0, True)
-add_model_to_registry(EfficientNetLiteB1, True)
-add_model_to_registry(EfficientNetLiteB2, True)
-add_model_to_registry(EfficientNetLiteB3, True)
-add_model_to_registry(EfficientNetLiteB4, True)
-add_model_to_registry(EfficientNetV2S, True)
-add_model_to_registry(EfficientNetV2M, True)
-add_model_to_registry(EfficientNetV2L, True)
-add_model_to_registry(EfficientNetV2XL, True)
-add_model_to_registry(EfficientNetV2B0, True)
-add_model_to_registry(EfficientNetV2B1, True)
-add_model_to_registry(EfficientNetV2B2, True)
-add_model_to_registry(EfficientNetV2B3, True)
-add_model_to_registry(TinyNetA, True)
-add_model_to_registry(TinyNetB, True)
-add_model_to_registry(TinyNetC, True)
-add_model_to_registry(TinyNetD, True)
-add_model_to_registry(TinyNetE, True)
+add_model_to_registry(EfficientNetB0, "imagenet")
+add_model_to_registry(EfficientNetB1, "imagenet")
+add_model_to_registry(EfficientNetB2, "imagenet")
+add_model_to_registry(EfficientNetB3, "imagenet")
+add_model_to_registry(EfficientNetB4, "imagenet")
+add_model_to_registry(EfficientNetB5, "imagenet")
+add_model_to_registry(EfficientNetB6, "imagenet")
+add_model_to_registry(EfficientNetB7, "imagenet")
+add_model_to_registry(EfficientNetLiteB0, "imagenet")
+add_model_to_registry(EfficientNetLiteB1, "imagenet")
+add_model_to_registry(EfficientNetLiteB2, "imagenet")
+add_model_to_registry(EfficientNetLiteB3, "imagenet")
+add_model_to_registry(EfficientNetLiteB4, "imagenet")
+add_model_to_registry(EfficientNetV2S, "imagenet")
+add_model_to_registry(EfficientNetV2M, "imagenet")
+add_model_to_registry(EfficientNetV2L, "imagenet")
+add_model_to_registry(EfficientNetV2XL, "imagenet")
+add_model_to_registry(EfficientNetV2B0, "imagenet")
+add_model_to_registry(EfficientNetV2B1, "imagenet")
+add_model_to_registry(EfficientNetV2B2, "imagenet")
+add_model_to_registry(EfficientNetV2B3, "imagenet")
+add_model_to_registry(TinyNetA, "imagenet")
+add_model_to_registry(TinyNetB, "imagenet")
+add_model_to_registry(TinyNetC, "imagenet")
+add_model_to_registry(TinyNetD, "imagenet")
+add_model_to_registry(TinyNetE, "imagenet")

kimm/models/efficientnet_test.py

@@ -48,15 +48,14 @@ def test_efficentnet_feature_extractor(
         self, model_class, width, fix_stem_channels
     ):
         x = random.uniform([1, 224, 224, 3]) * 255.0
-        model = model_class(
-            input_shape=[224, 224, 3], as_feature_extractor=True
-        )
+        model = model_class(input_shape=[224, 224, 3], feature_extractor=True)
 
         y = model(x, training=False)
 
         self.assertIsInstance(y, dict)
-        self.assertAllEqual(
-            list(y.keys()), model_class.available_feature_keys()
+        self.assertContainsSubset(
+            model_class.available_feature_keys(),
+            list(y.keys()),
         )
         if fix_stem_channels:
             self.assertEqual(list(y["STEM_S2"].shape), [1, 112, 112, 32])
@@ -86,15 +85,14 @@ def test_efficentnet_feature_extractor(
     )
     def test_efficentnet_v2_feature_extractor(self, model_class, width):
         x = random.uniform([1, 224, 224, 3]) * 255.0
-        model = model_class(
-            input_shape=[224, 224, 3], as_feature_extractor=True
-        )
+        model = model_class(input_shape=[224, 224, 3], feature_extractor=True)
 
         y = model(x, training=False)
 
         self.assertIsInstance(y, dict)
-        self.assertAllEqual(
-            list(y.keys()), model_class.available_feature_keys()
+        self.assertContainsSubset(
+            model_class.available_feature_keys(),
+            list(y.keys()),
         )
         if "EfficientNetV2S" in model_class.__name__:
             self.assertEqual(list(y["STEM_S2"].shape), [1, 112, 112, 24])

kimm/models/ghostnet.py

@@ -595,9 +595,9 @@ def __init__(
         )
 
 
-add_model_to_registry(GhostNet050, False)
-add_model_to_registry(GhostNet100, True)
-add_model_to_registry(GhostNet130, True)
-add_model_to_registry(GhostNet100V2, True)
-add_model_to_registry(GhostNet130V2, True)
-add_model_to_registry(GhostNet160V2, True)
+add_model_to_registry(GhostNet050)
+add_model_to_registry(GhostNet100, "imagenet")
+add_model_to_registry(GhostNet130, "imagenet")
+add_model_to_registry(GhostNet100V2, "imagenet")
+add_model_to_registry(GhostNet130V2, "imagenet")
+add_model_to_registry(GhostNet160V2, "imagenet")

kimm/models/ghostnet_test.py

@@ -22,13 +22,14 @@ def test_ghostnet_base(self, model_class):
     @parameterized.named_parameters([(GhostNet100.__name__, GhostNet100)])
     def test_ghostnet_feature_extractor(self, model_class):
         x = random.uniform([1, 224, 224, 3]) * 255.0
-        model = model_class(as_feature_extractor=True)
+        model = model_class(feature_extractor=True)
 
         y = model(x, training=False)
 
         self.assertIsInstance(y, dict)
-        self.assertAllEqual(
-            list(y.keys()), model_class.available_feature_keys()
+        self.assertContainsSubset(
+            model_class.available_feature_keys(),
+            list(y.keys()),
         )
         self.assertEqual(list(y["STEM_S2"].shape), [1, 112, 112, 16])
         self.assertEqual(list(y["BLOCK1_S4"].shape), [1, 56, 56, 24])
@@ -49,13 +50,14 @@ def test_ghostnetv2_base(self, model_class):
     @parameterized.named_parameters([(GhostNet100V2.__name__, GhostNet100V2)])
     def test_ghostnetv2_feature_extractor(self, model_class):
         x = random.uniform([1, 224, 224, 3]) * 255.0
-        model = model_class(as_feature_extractor=True)
+        model = model_class(feature_extractor=True)
 
         y = model(x, training=False)
 
         self.assertIsInstance(y, dict)
-        self.assertAllEqual(
-            list(y.keys()), model_class.available_feature_keys()
+        self.assertContainsSubset(
+            model_class.available_feature_keys(),
+            list(y.keys()),
         )
         self.assertEqual(list(y["STEM_S2"].shape), [1, 112, 112, 16])
         self.assertEqual(list(y["BLOCK1_S4"].shape), [1, 56, 56, 24])