Migrate Efficientnetv1 to Backbone (#1716)

* created new files

* moved old files to new one

* efficientnetv1 backbone, presets, and imports updated

* fix imports

* fix imports

* added preset unit test

* updated backbone

* fix init

* fix legacy init

* fix aliases presets

* fix backbone round filter argument

* fix depthwise conv

* fix conv kernel initializer

* fix depthwiseconv

* fix scope name match pattern error

* fix scope name

* fix block name

* remove block args preset

* remove model_name

* remove default_size from preset

* updated test cases

* updated docs

* fix id_skip

* fix test

* fix format

* reviewd comments

* fix format

* fix typo

* fix naming

* test with layer-matching

* fix typo

* fix format

* backbone test updated

* review changes

* port

* fix port

* fix port 2

* port: fix argument

* port: final fix

* port:docs typo

* port: update

* review comment

* format

keras_cv/models/__init__.py

@@ -61,6 +61,33 @@
 from keras_cv.models.backbones.efficientnet_lite.efficientnet_lite_backbone import (  # noqa: E501
     EfficientNetLiteBackbone,
 )
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B0Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B1Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B2Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B3Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B4Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B5Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B6Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1B7Backbone,
+)
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_aliases import (
+    EfficientNetV1Backbone,
+)
 from keras_cv.models.backbones.efficientnet_v2.efficientnet_v2_aliases import (
     EfficientNetV2B0Backbone,
 )

keras_cv/models/backbones/backbone_presets.py

@@ -19,6 +19,9 @@
 from keras_cv.models.backbones.efficientnet_lite import (
     efficientnet_lite_backbone_presets,
 )
+from keras_cv.models.backbones.efficientnet_v1 import (
+    efficientnet_v1_backbone_presets,
+)
 from keras_cv.models.backbones.efficientnet_v2 import (
     efficientnet_v2_backbone_presets,
 )
@@ -32,6 +35,7 @@
     **resnet_v2_backbone_presets.backbone_presets_no_weights,
     **mobilenet_v3_backbone_presets.backbone_presets_no_weights,
     **csp_darknet_backbone_presets.backbone_presets_no_weights,
+    **efficientnet_v1_backbone_presets.backbone_presets_no_weights,
     **efficientnet_v2_backbone_presets.backbone_presets_no_weights,
     **densenet_backbone_presets.backbone_presets_no_weights,
     **efficientnet_lite_backbone_presets.backbone_presets_no_weights,
@@ -43,6 +47,7 @@
     **resnet_v2_backbone_presets.backbone_presets_with_weights,
     **mobilenet_v3_backbone_presets.backbone_presets_with_weights,
     **csp_darknet_backbone_presets.backbone_presets_with_weights,
+    **efficientnet_v1_backbone_presets.backbone_presets_with_weights,
     **efficientnet_v2_backbone_presets.backbone_presets_with_weights,
     **densenet_backbone_presets.backbone_presets_with_weights,
     **efficientnet_lite_backbone_presets.backbone_presets_with_weights,

keras_cv/models/backbones/efficientnet_v1/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2023 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

keras_cv/models/backbones/efficientnet_v1/efficientnet_v1_aliases.py

@@ -0,0 +1,315 @@
+# Copyright 2023 The KerasCV Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from keras_cv.models.backbones.efficientnet_v1.efficientnet_v1_backbone import (
+    EfficientNetV1Backbone,
+)
+from keras_cv.utils.python_utils import classproperty
+
+ALIAS_DOCSTRING = """Instantiates the {name} architecture.
+
+    Reference:
+    - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
+      (ICML 2019)
+
+    Args:
+        include_rescaling: bool, whether to rescale the inputs. If set
+            to `True`, inputs will be passed through a `Rescaling(1/255.0)`
+            layer.
+        input_shape: optional shape tuple, defaults to (None, None, 3).
+        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
+            to use as image input for the model.
+"""  # noqa: E501
+
+
+class EfficientNetV1B0Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b0", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+class EfficientNetV1B1Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b1", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+class EfficientNetV1B2Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b2", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+class EfficientNetV1B3Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b3", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+class EfficientNetV1B4Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b4", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+class EfficientNetV1B5Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b5", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+class EfficientNetV1B6Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b6", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+class EfficientNetV1B7Backbone(EfficientNetV1Backbone):
+    def __new__(
+        cls,
+        include_rescaling=True,
+        input_shape=(None, None, 3),
+        input_tensor=None,
+        **kwargs,
+    ):
+        # Pack args in kwargs
+        kwargs.update(
+            {
+                "include_rescaling": include_rescaling,
+                "input_shape": input_shape,
+                "input_tensor": input_tensor,
+            }
+        )
+        return EfficientNetV1Backbone.from_preset("efficientnetv1_b7", **kwargs)
+
+    @classproperty
+    def presets(cls):
+        """Dictionary of preset names and configurations."""
+        return {}
+
+    @classproperty
+    def presets_with_weights(cls):
+        """Dictionary of preset names and configurations that include
+        weights."""
+        return {}
+
+
+setattr(
+    EfficientNetV1B0Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B0"),
+)
+setattr(
+    EfficientNetV1B1Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B1"),
+)
+setattr(
+    EfficientNetV1B2Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B2"),
+)
+setattr(
+    EfficientNetV1B3Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B3"),
+)
+setattr(
+    EfficientNetV1B4Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B4"),
+)
+setattr(
+    EfficientNetV1B5Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B5"),
+)
+setattr(
+    EfficientNetV1B6Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B6"),
+)
+setattr(
+    EfficientNetV1B7Backbone,
+    "__doc__",
+    ALIAS_DOCSTRING.format(name="EfficientNetV1B7"),
+)