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Refactor: commented out ViT-related mentions in files #223

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merged 5 commits into from
Jul 30, 2024
Merged

Refactor: commented out ViT-related mentions in files #223

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dd1b77a
refactor: commented out all ViT mentions in files
Jul 10, 2024
9289d90
refactor(tests): commented out ViT-related unittests
Jul 10, 2024
437d3d0
refactor: update imports and comments, remove deprecated dependencies
Jul 16, 2024
6ab16d3
fix(metrics): remove num_classes parameter from F1Score and update re…
Jul 17, 2024
8bbd83c
refactor(metrics): delete F1Score in the AutoML training block
Jul 26, 2024
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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ The open-source software AUCMEDI allows fast setup of medical image classificati
- Wide range of 2D/3D data entry options with interfaces to the most common medical image formats such as DICOM, MetaImage, NifTI, PNG or TIF already supplied.
- Selection of pre-processing methods for preparing images, such as augmentation processes, color conversions, windowing, filtering, resizing and normalization.
- Use of deep neural networks for binary, multi-class as well as multi-label classification and efficient methods against class imbalances using modern loss functions such as focal loss.
- Library from modern architectures, like ResNet up to EfficientNet and Vision-Transformers (ViT)⁠.
- Library from modern architectures, like ResNet up to ConvNeXt. <!-- and Vision-Transformers (ViT)⁠.-->
- Complex ensemble learning techniques (combination of predictions) using test-time augmentation, bagging via cross-validation or stacking via logistic regressions.
- Explainable AI to explain opaque decision-making processes of the models using activation maps such as Grad-CAM or backpropagation.
- Automated Machine Learning (AutoML) mentality to ensure easy deployment, integration and maintenance of complex medical image classification pipelines (Docker).
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3 changes: 1 addition & 2 deletions aucmedi/automl/block_train.py
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Expand Up @@ -23,8 +23,7 @@
import os
import numpy as np
import json
from tensorflow.keras.metrics import AUC
from tensorflow_addons.metrics import F1Score
from tensorflow.keras.metrics import AUC, F1Score
from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, \
ReduceLROnPlateau, EarlyStopping
# Internal libraries
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24 changes: 12 additions & 12 deletions aucmedi/neural_network/architectures/image/__init__.py
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Expand Up @@ -61,10 +61,10 @@
# Xception
from aucmedi.neural_network.architectures.image.xception import Xception
# Vision Transformer (ViT)
from aucmedi.neural_network.architectures.image.vit_b16 import ViT_B16
from aucmedi.neural_network.architectures.image.vit_b32 import ViT_B32
from aucmedi.neural_network.architectures.image.vit_l16 import ViT_L16
from aucmedi.neural_network.architectures.image.vit_l32 import ViT_L32
# from aucmedi.neural_network.architectures.image.vit_b16 import ViT_B16
# from aucmedi.neural_network.architectures.image.vit_b32 import ViT_B32
# from aucmedi.neural_network.architectures.image.vit_l16 import ViT_L16
# from aucmedi.neural_network.architectures.image.vit_l32 import ViT_L32
# ConvNeXt
from aucmedi.neural_network.architectures.image.convnext_base import ConvNeXtBase
from aucmedi.neural_network.architectures.image.convnext_tiny import ConvNeXtTiny
Expand Down Expand Up @@ -103,10 +103,10 @@
"VGG16": VGG16,
"VGG19": VGG19,
"Xception": Xception,
"ViT_B16": ViT_B16,
"ViT_B32": ViT_B32,
"ViT_L16": ViT_L16,
"ViT_L32": ViT_L32,
# "ViT_B16": ViT_B16,
# "ViT_B32": ViT_B32,
# "ViT_L16": ViT_L16,
# "ViT_L32": ViT_L32,
"ConvNeXtBase": ConvNeXtBase,
"ConvNeXtTiny": ConvNeXtTiny,
"ConvNeXtSmall": ConvNeXtSmall,
Expand Down Expand Up @@ -190,10 +190,10 @@
"VGG16": "caffe",
"VGG19": "caffe",
"Xception": "tf",
"ViT_B16": "tf",
"ViT_B32": "tf",
"ViT_L16": "tf",
"ViT_L32": "tf",
# "ViT_B16": "tf",
# "ViT_B32": "tf",
# "ViT_L16": "tf",
# "ViT_L32": "tf",
"ConvNeXtBase": None,
"ConvNeXtTiny": None,
"ConvNeXtSmall": None,
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2 changes: 0 additions & 2 deletions requirements.txt
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Here, the commented vit-keras is missing, but is fine and could be added later if vit-keras gets hopefully an update in the future.

Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,6 @@ scikit-image==0.21.0
lime==0.2.0.1
pooch==1.6.0
classification-models-3D==1.0.10
vit-keras==0.1.2
tensorflow-addons==0.21.0
Keras-Applications==1.0.8
SimpleITK==2.2.0
batchgenerators==0.25
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3 changes: 1 addition & 2 deletions setup.py
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Expand Up @@ -35,8 +35,7 @@
'lime>=0.2.0.1',
'pooch>=1.6.0',
'classification-models-3D>=1.0.10',
'vit-keras>=0.1.2',
'tensorflow-addons>=0.21.0',
# 'vit-keras>=0.1.2',
'Keras-Applications==1.0.8',
'SimpleITK>=2.2.0',
'batchgenerators>=0.25',
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32 changes: 16 additions & 16 deletions tests/test_architectures_image.py
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Expand Up @@ -665,7 +665,7 @@ def test_Xception(self):
# Architecture: ViT B16 #
#-------------------------------------------------#
# Functionality and Interoperability testing deactived due to too intensive RAM requirements
def test_ViT_B16(self):
# def test_ViT_B16(self):
# self.datagen_RGB.sf_resize = Resize(shape=(224, 224))
# arch = ViT_B16(Classifier(n_labels=4), channels=3,
# input_shape=(224, 224))
Expand All @@ -676,16 +676,16 @@ def test_ViT_B16(self):
# batch_queue_size=1, input_shape=(224, 224))
# try : model.model.summary()
# except : raise Exception()
self.assertTrue(supported_standardize_mode["ViT_B16"] == "tf")
self.assertTrue(sdm_global["2D.ViT_B16"] == "tf")
self.assertTrue("2D.ViT_B16" in architecture_dict)
# self.assertTrue(supported_standardize_mode["ViT_B16"] == "tf")
# self.assertTrue(sdm_global["2D.ViT_B16"] == "tf")
# self.assertTrue("2D.ViT_B16" in architecture_dict)
# self.datagen_RGB.sf_resize = Resize(shape=(32, 32))

#-------------------------------------------------#
# Architecture: ViT B32 #
#-------------------------------------------------#
# Functionality and Interoperability testing deactived due to too intensive RAM requirements
def test_ViT_B32(self):
# def test_ViT_B32(self):
# self.datagen_RGB.sf_resize = Resize(shape=(224, 224))
# arch = ViT_B32(Classifier(n_labels=4), channels=3,
# input_shape=(224, 224))
Expand All @@ -696,16 +696,16 @@ def test_ViT_B32(self):
# batch_queue_size=1, input_shape=(224, 224))
# try : model.model.summary()
# except : raise Exception()
self.assertTrue(supported_standardize_mode["ViT_B32"] == "tf")
self.assertTrue(sdm_global["2D.ViT_B32"] == "tf")
self.assertTrue("2D.ViT_B32" in architecture_dict)
# self.assertTrue(supported_standardize_mode["ViT_B32"] == "tf")
# self.assertTrue(sdm_global["2D.ViT_B32"] == "tf")
# self.assertTrue("2D.ViT_B32" in architecture_dict)
# self.datagen_RGB.sf_resize = Resize(shape=(32, 32))

#-------------------------------------------------#
# Architecture: ViT L16 #
#-------------------------------------------------#
# Functionality and Interoperability testing deactived due to too intensive RAM requirements
def test_ViT_L16(self):
# def test_ViT_L16(self):
# self.datagen_RGB.sf_resize = Resize(shape=(384, 384))
# arch = ViT_L16(Classifier(n_labels=4), channels=3,
# input_shape=(384, 384))
Expand All @@ -716,16 +716,16 @@ def test_ViT_L16(self):
# batch_queue_size=1, input_shape=(384, 384))
# try : model.model.summary()
# except : raise Exception()
self.assertTrue(supported_standardize_mode["ViT_L16"] == "tf")
self.assertTrue(sdm_global["2D.ViT_L16"] == "tf")
self.assertTrue("2D.ViT_L16" in architecture_dict)
# self.assertTrue(supported_standardize_mode["ViT_L16"] == "tf")
# self.assertTrue(sdm_global["2D.ViT_L16"] == "tf")
# self.assertTrue("2D.ViT_L16" in architecture_dict)
# self.datagen_RGB.sf_resize = Resize(shape=(32, 32))

#-------------------------------------------------#
# Architecture: ViT L32 #
#-------------------------------------------------#
# Functionality and Interoperability testing deactived due to too intensive RAM requirements
def test_ViT_L32(self):
# def test_ViT_L32(self):
# self.datagen_RGB.sf_resize = Resize(shape=(384, 384))
# arch = ViT_L32(Classifier(n_labels=4), channels=3,
# input_shape=(384, 384))
Expand All @@ -736,9 +736,9 @@ def test_ViT_L32(self):
# batch_queue_size=1, input_shape=(384, 384))
# try : model.model.summary()
# except : raise Exception()
self.assertTrue(supported_standardize_mode["ViT_L32"] == "tf")
self.assertTrue(sdm_global["2D.ViT_L32"] == "tf")
self.assertTrue("2D.ViT_L32" in architecture_dict)
# self.assertTrue(supported_standardize_mode["ViT_L32"] == "tf")
# self.assertTrue(sdm_global["2D.ViT_L32"] == "tf")
# self.assertTrue("2D.ViT_L32" in architecture_dict)
# self.datagen_RGB.sf_resize = Resize(shape=(32, 32))

#-------------------------------------------------#
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