feat(Architecture): added ConvNeXt 3D architectures

aucmedi/neural_network/architectures/volume/convnext_base.py

@@ -0,0 +1,88 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                    Documentation                    #
+#-----------------------------------------------------#
+""" The classification variant of the ConvNeXt Base architecture.
+
+| Architecture Variable    | Value                      |
+| ------------------------ | -------------------------- |
+| Key in architecture_dict | "3D.ConvNeXtBase"          |
+| Input_shape              | (64, 64, 64)               |
+| Standardization          | None                       |
+
+!!! warning
+     ConvNeXt models expect their inputs to be float or uint8 tensors of pixels with values in the [0-255] range.
+     Standardization is applied inside the architecture.
+
+???+ abstract "Reference - Implementation"
+    Solovyev, Roman & Kalinin, Alexandr & Gabruseva, Tatiana. (2021). <br>
+    3D Convolutional Neural Networks for Stalled Brain Capillary Detection. <br>
+    [https://github.com/ZFTurbo/classification_models_3D](https://github.com/ZFTurbo/classification_models_3D) <br>
+
+???+ abstract "Reference - Publication"
+    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
+    10 Jan 2022. A ConvNet for the 2020s.
+    <br>
+    [https://arxiv.org/abs/2201.03545](https://arxiv.org/abs/2201.03545)
+"""
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External libraries
+from classification_models_3D.tfkeras import Classifiers
+# Internal libraries
+from aucmedi.neural_network.architectures import Architecture_Base
+
+#-----------------------------------------------------#
+#          Architecture class: ConvNeXt Base          #
+#-----------------------------------------------------#
+class ConvNeXtBase(Architecture_Base):
+    #---------------------------------------------#
+    #                Initialization               #
+    #---------------------------------------------#
+    def __init__(self, classification_head, channels, input_shape=(64, 64, 64),
+                 pretrained_weights=False, preprocessing=True):
+        self.classifier = classification_head
+        self.input = input_shape + (channels,)
+        self.pretrained_weights = pretrained_weights
+        self.preprocessing = preprocessing
+
+    #---------------------------------------------#
+    #                Create Model                 #
+    #---------------------------------------------#
+    def create_model(self):
+        # Get pretrained image weights from imagenet if desired
+        if self.pretrained_weights : model_weights = "imagenet"
+        else : model_weights = None
+
+        # Obtain ConvNeXtBase as base model
+        BaseModel, preprocess_input = Classifiers.get("convnext_base")
+        base_model = BaseModel(include_top=False, weights=model_weights,
+                               input_tensor=None, input_shape=self.input,
+                               pooling=None, 
+                               include_preprocessing=self.preprocessing)
+        top_model = base_model.output
+
+        # Add classification head
+        model = self.classifier.build(model_input=base_model.input,
+                                      model_output=top_model)
+
+        # Return created model
+        return model

aucmedi/neural_network/architectures/volume/convnext_large.py

@@ -0,0 +1,88 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                    Documentation                    #
+#-----------------------------------------------------#
+""" The classification variant of the ConvNeXt Large architecture.
+
+| Architecture Variable    | Value                      |
+| ------------------------ | -------------------------- |
+| Key in architecture_dict | "3D.ConvNeXtLarge"         |
+| Input_shape              | (64, 64, 64)               |
+| Standardization          | None                       |
+
+!!! warning
+     ConvNeXt models expect their inputs to be float or uint8 tensors of pixels with values in the [0-255] range.
+     Standardization is applied inside the architecture.
+
+???+ abstract "Reference - Implementation"
+    Solovyev, Roman & Kalinin, Alexandr & Gabruseva, Tatiana. (2021). <br>
+    3D Convolutional Neural Networks for Stalled Brain Capillary Detection. <br>
+    [https://github.com/ZFTurbo/classification_models_3D](https://github.com/ZFTurbo/classification_models_3D) <br>
+
+???+ abstract "Reference - Publication"
+    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
+    10 Jan 2022. A ConvNet for the 2020s.
+    <br>
+    [https://arxiv.org/abs/2201.03545](https://arxiv.org/abs/2201.03545)
+"""
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External libraries
+from classification_models_3D.tfkeras import Classifiers
+# Internal libraries
+from aucmedi.neural_network.architectures import Architecture_Base
+
+#-----------------------------------------------------#
+#          Architecture class: ConvNeXt Large         #
+#-----------------------------------------------------#
+class ConvNeXtLarge(Architecture_Base):
+    #---------------------------------------------#
+    #                Initialization               #
+    #---------------------------------------------#
+    def __init__(self, classification_head, channels, input_shape=(64, 64, 64),
+                 pretrained_weights=False, preprocessing=True):
+        self.classifier = classification_head
+        self.input = input_shape + (channels,)
+        self.pretrained_weights = pretrained_weights
+        self.preprocessing = preprocessing
+
+    #---------------------------------------------#
+    #                Create Model                 #
+    #---------------------------------------------#
+    def create_model(self):
+        # Get pretrained image weights from imagenet if desired
+        if self.pretrained_weights : model_weights = "imagenet"
+        else : model_weights = None
+
+        # Obtain ConvNeXtLarge as base model
+        BaseModel, preprocess_input = Classifiers.get("convnext_large")
+        base_model = BaseModel(include_top=False, weights=model_weights,
+                               input_tensor=None, input_shape=self.input,
+                               pooling=None, 
+                               include_preprocessing=self.preprocessing)
+        top_model = base_model.output
+
+        # Add classification head
+        model = self.classifier.build(model_input=base_model.input,
+                                      model_output=top_model)
+
+        # Return created model
+        return model

aucmedi/neural_network/architectures/volume/convnext_small.py

@@ -0,0 +1,88 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                    Documentation                    #
+#-----------------------------------------------------#
+""" The classification variant of the ConvNeXt Small architecture.
+
+| Architecture Variable    | Value                      |
+| ------------------------ | -------------------------- |
+| Key in architecture_dict | "3D.ConvNeXtSmall"          |
+| Input_shape              | (64, 64, 64)               |
+| Standardization          | None                       |
+
+!!! warning
+     ConvNeXt models expect their inputs to be float or uint8 tensors of pixels with values in the [0-255] range.
+     Standardization is applied inside the architecture.
+
+???+ abstract "Reference - Implementation"
+    Solovyev, Roman & Kalinin, Alexandr & Gabruseva, Tatiana. (2021). <br>
+    3D Convolutional Neural Networks for Stalled Brain Capillary Detection. <br>
+    [https://github.com/ZFTurbo/classification_models_3D](https://github.com/ZFTurbo/classification_models_3D) <br>
+
+???+ abstract "Reference - Publication"
+    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
+    10 Jan 2022. A ConvNet for the 2020s.
+    <br>
+    [https://arxiv.org/abs/2201.03545](https://arxiv.org/abs/2201.03545)
+"""
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External libraries
+from classification_models_3D.tfkeras import Classifiers
+# Internal libraries
+from aucmedi.neural_network.architectures import Architecture_Base
+
+#-----------------------------------------------------#
+#         Architecture class: ConvNeXt Small          #
+#-----------------------------------------------------#
+class ConvNeXtSmall(Architecture_Base):
+    #---------------------------------------------#
+    #                Initialization               #
+    #---------------------------------------------#
+    def __init__(self, classification_head, channels, input_shape=(64, 64, 64),
+                 pretrained_weights=False, preprocessing=True):
+        self.classifier = classification_head
+        self.input = input_shape + (channels,)
+        self.pretrained_weights = pretrained_weights
+        self.preprocessing = preprocessing
+
+    #---------------------------------------------#
+    #                Create Model                 #
+    #---------------------------------------------#
+    def create_model(self):
+        # Get pretrained image weights from imagenet if desired
+        if self.pretrained_weights : model_weights = "imagenet"
+        else : model_weights = None
+
+        # Obtain ConvNeXtSmall as base model
+        BaseModel, preprocess_input = Classifiers.get("convnext_small")
+        base_model = BaseModel(include_top=False, weights=model_weights,
+                               input_tensor=None, input_shape=self.input,
+                               pooling=None, 
+                               include_preprocessing=self.preprocessing)
+        top_model = base_model.output
+
+        # Add classification head
+        model = self.classifier.build(model_input=base_model.input,
+                                      model_output=top_model)
+
+        # Return created model
+        return model

aucmedi/neural_network/architectures/volume/convnext_tiny.py

@@ -0,0 +1,88 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                    Documentation                    #
+#-----------------------------------------------------#
+""" The classification variant of the ConvNeXt Tiny architecture.
+
+| Architecture Variable    | Value                      |
+| ------------------------ | -------------------------- |
+| Key in architecture_dict | "3D.ConvNeXtTiny"          |
+| Input_shape              | (64, 64, 64)               |
+| Standardization          | None                       |
+
+!!! warning
+     ConvNeXt models expect their inputs to be float or uint8 tensors of pixels with values in the [0-255] range.
+     Standardization is applied inside the architecture.
+
+???+ abstract "Reference - Implementation"
+    Solovyev, Roman & Kalinin, Alexandr & Gabruseva, Tatiana. (2021). <br>
+    3D Convolutional Neural Networks for Stalled Brain Capillary Detection. <br>
+    [https://github.com/ZFTurbo/classification_models_3D](https://github.com/ZFTurbo/classification_models_3D) <br>
+
+???+ abstract "Reference - Publication"
+    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
+    10 Jan 2022. A ConvNet for the 2020s.
+    <br>
+    [https://arxiv.org/abs/2201.03545](https://arxiv.org/abs/2201.03545)
+"""
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External libraries
+from classification_models_3D.tfkeras import Classifiers
+# Internal libraries
+from aucmedi.neural_network.architectures import Architecture_Base
+
+#-----------------------------------------------------#
+#          Architecture class: ConvNeXt Tiny          #
+#-----------------------------------------------------#
+class ConvNeXtTiny(Architecture_Base):
+    #---------------------------------------------#
+    #                Initialization               #
+    #---------------------------------------------#
+    def __init__(self, classification_head, channels, input_shape=(64, 64, 64),
+                 pretrained_weights=False, preprocessing=True):
+        self.classifier = classification_head
+        self.input = input_shape + (channels,)
+        self.pretrained_weights = pretrained_weights
+        self.preprocessing = preprocessing
+
+    #---------------------------------------------#
+    #                Create Model                 #
+    #---------------------------------------------#
+    def create_model(self):
+        # Get pretrained image weights from imagenet if desired
+        if self.pretrained_weights : model_weights = "imagenet"
+        else : model_weights = None
+
+        # Obtain ConvNeXtTiny as base model
+        BaseModel, preprocess_input = Classifiers.get("convnext_tiny")
+        base_model = BaseModel(include_top=False, weights=model_weights,
+                               input_tensor=None, input_shape=self.input,
+                               pooling=None, 
+                               include_preprocessing=self.preprocessing)
+        top_model = base_model.output
+
+        # Add classification head
+        model = self.classifier.build(model_input=base_model.input,
+                                      model_output=top_model)
+
+        # Return created model
+        return model