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feat(Architecture): added ConvNeXt - closes #101
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85
aucmedi/neural_network/architectures/image/convnext_base.py
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|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| #==============================================================================# | ||
| # 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 | "2D.ConvNeXtBase" | | ||
| | Input_shape | (224, 224) | | ||
| | Standardization | None | | ||
| Recommended alternative `Input_shape` is 384x384 pixels. | ||
| !!! 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" | ||
| [https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext](https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext) <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 tensorflow.keras.applications.convnext import ConvNeXtBase as BaseModel | ||
| # Internal libraries | ||
| from aucmedi.neural_network.architectures import Architecture_Base | ||
|
|
||
| #-----------------------------------------------------# | ||
| # Architecture class: ConvNeXtBase # | ||
| #-----------------------------------------------------# | ||
| class ConvNeXtBase(Architecture_Base): | ||
| #---------------------------------------------# | ||
| # Initialization # | ||
| #---------------------------------------------# | ||
| def __init__(self, classification_head, channels, input_shape=(224, 224), | ||
| pretrained_weights=False): | ||
| self.classifier = classification_head | ||
| self.input = input_shape + (channels,) | ||
| self.pretrained_weights = pretrained_weights | ||
|
|
||
| #---------------------------------------------# | ||
| # 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 ResNet50 as base model | ||
| base_model = BaseModel(include_top=False, weights=model_weights, | ||
| input_tensor=None, input_shape=self.input, | ||
| pooling=None) | ||
| 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 |
85 changes: 85 additions & 0 deletions
85
aucmedi/neural_network/architectures/image/convnext_large.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| #==============================================================================# | ||
| # 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 | "2D.ConvNeXtLarge" | | ||
| | Input_shape | (384x384) | | ||
| | Standardization | None | | ||
| Recommended alternative `Input_shape` is 224x224 pixels. | ||
| !!! 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" | ||
| [https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext](https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext) <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 tensorflow.keras.applications.convnext import ConvNeXtLarge as BaseModel | ||
| # Internal libraries | ||
| from aucmedi.neural_network.architectures import Architecture_Base | ||
|
|
||
| #-----------------------------------------------------# | ||
| # Architecture class: ConvNeXtLarge # | ||
| #-----------------------------------------------------# | ||
| class ConvNeXtLarge(Architecture_Base): | ||
| #---------------------------------------------# | ||
| # Initialization # | ||
| #---------------------------------------------# | ||
| def __init__(self, classification_head, channels, input_shape=(224, 224), | ||
| pretrained_weights=False): | ||
| self.classifier = classification_head | ||
| self.input = input_shape + (channels,) | ||
| self.pretrained_weights = pretrained_weights | ||
|
|
||
| #---------------------------------------------# | ||
| # 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 ResNet50 as base model | ||
| base_model = BaseModel(include_top=False, weights=model_weights, | ||
| input_tensor=None, input_shape=self.input, | ||
| pooling=None) | ||
| 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 |
83 changes: 83 additions & 0 deletions
83
aucmedi/neural_network/architectures/image/convnext_small.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,83 @@ | ||
| #==============================================================================# | ||
| # 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 | "2D.ConvNeXtSmall" | | ||
| | Input_shape | (224, 224) | | ||
| | 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" | ||
| [https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext](https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext) <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 tensorflow.keras.applications.convnext import ConvNeXtSmall as BaseModel | ||
| # Internal libraries | ||
| from aucmedi.neural_network.architectures import Architecture_Base | ||
|
|
||
| #-----------------------------------------------------# | ||
| # Architecture class: ConvNeXtSmall # | ||
| #-----------------------------------------------------# | ||
| class ConvNeXtSmall(Architecture_Base): | ||
| #---------------------------------------------# | ||
| # Initialization # | ||
| #---------------------------------------------# | ||
| def __init__(self, classification_head, channels, input_shape=(224, 224), | ||
| pretrained_weights=False): | ||
| self.classifier = classification_head | ||
| self.input = input_shape + (channels,) | ||
| self.pretrained_weights = pretrained_weights | ||
|
|
||
| #---------------------------------------------# | ||
| # 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 ResNet50 as base model | ||
| base_model = BaseModel(include_top=False, weights=model_weights, | ||
| input_tensor=None, input_shape=self.input, | ||
| pooling=None) | ||
| 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 |
83 changes: 83 additions & 0 deletions
83
aucmedi/neural_network/architectures/image/convnext_tiny.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,83 @@ | ||
| #==============================================================================# | ||
| # 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 | "2D.ConvNeXtTiny" | | ||
| | Input_shape | (224, 224) | | ||
| | 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" | ||
| [https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext](https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext) <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 tensorflow.keras.applications.convnext import ConvNeXtTiny as BaseModel | ||
| # Internal libraries | ||
| from aucmedi.neural_network.architectures import Architecture_Base | ||
|
|
||
| #-----------------------------------------------------# | ||
| # Architecture class: ConvNeXtTiny # | ||
| #-----------------------------------------------------# | ||
| class ConvNeXtTiny(Architecture_Base): | ||
| #---------------------------------------------# | ||
| # Initialization # | ||
| #---------------------------------------------# | ||
| def __init__(self, classification_head, channels, input_shape=(224, 224), | ||
| pretrained_weights=False): | ||
| self.classifier = classification_head | ||
| self.input = input_shape + (channels,) | ||
| self.pretrained_weights = pretrained_weights | ||
|
|
||
| #---------------------------------------------# | ||
| # 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 ResNet50 as base model | ||
| base_model = BaseModel(include_top=False, weights=model_weights, | ||
| input_tensor=None, input_shape=self.input, | ||
| pooling=None) | ||
| 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 |