[YOLOv8] Step 0/? Addition of SPP and SPPF Layer

keras_cv/models/object_detection/yolov8/__init__.py

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+# 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/object_detection/yolov8/__internal__/__init__.py

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+# 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/object_detection/yolov8/__internal__/layers/__init__.py

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+# 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.object_detection.yolov8.__internal__.layers.sppf import SPP
+from keras_cv.models.object_detection.yolov8.__internal__.layers.sppf import (
+    SPPF,
+)

keras_cv/models/object_detection/yolov8/__internal__/layers/sppf.py

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+# 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.
+
+import tensorflow as tf
+from tensorflow.keras import layers
+
+
+@tf.keras.utils.register_keras_serializable(package="keras_cv")
+class SPP(layers.Layer):
+    """
+    Performs Spatial Pyramid Pooling.
+    """
+
+    def __init__(self, filters, kernels=(5, 9, 13), **kwargs):
+        super().__init__(**kwargs)
+        self.filters = filters
+        self.kernels = kernels
+        self.num_filters = filters // 2
+        self.conv1 = layers.Conv2D(
+            filters=self.num_filters, kernel_size=1, strides=1
+        )
+        self.conv2 = layers.Conv2D(
+            filters=self.filters, kernel_size=1, strides=1
+        )
+        self.modules = [
+            layers.MaxPool2D(pool_size=x, strides=1, padding="SAME")
+            for x in self.kernels
+        ]
+
+    def call(self, x):
+        x = self.conv1(x)
+        return self.conv2(
+            tf.concat([x] + [module(x) for module in self.modules], axis=-1)
+        )
+
+    def get_config(self):
+        config = {
+            "filters": self.filters,
+            "kernels": self.kernels,
+            "num_filters": self.num_filters,
+        }
+        base_config = super().get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+
+@tf.keras.utils.register_keras_serializable(package="keras_cv")
+class SPPF(layers.Layer):
+    """
+    Performs Spatial Pyramid Pooling.
+    """
+    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+    def __init__(self, filters, kernel=5, **kwargs):
+        super().__init__(**kwargs)
+        self.filters = filters
+        self.kernel = kernel
+        self.num_filters = filters // 2
+        self.conv1 = layers.Conv2D(
+            filters=self.num_filters, kernel_size=1, strides=1
+        )
+        self.conv2 = layers.Conv2D(
+            filters=self.filters, kernel_size=1, strides=1
+        )
+        self.module = layers.MaxPool2D(
+            pool_size=self.kernel, strides=1, padding="SAME"
+        )
+
+    def call(self, x):
+        x = self.conv1(x)
+        x1 = self.module(x)
+        x2 = self.module(x1)
+        return self.conv2(tf.concat([x, x1, x2, self.module(x2)], axis=-1))
+
+    def get_config(self):
+        config = {
+            "filters": self.filters,
+            "kernel": self.kernel,
+            "num_filters": self.num_filters,
+        }
+        base_config = super().get_config()
+        return dict(list(base_config.items()) + list(config.items()))

keras_cv/models/object_detection/yolov8/yolov8.py

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+# 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.