feat(Architectures): removed 2D ResNeXt image architecture support

frankkramer-lab · Feb 21, 2024 · 979a660 · 979a660
1 parent f6945b8
commit 979a660
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diff --git a/aucmedi/neural_network/architectures/image/__init__.py b/aucmedi/neural_network/architectures/image/__init__.py
@@ -49,9 +49,6 @@
 from aucmedi.neural_network.architectures.image.resnet50v2 import ResNet50V2
 from aucmedi.neural_network.architectures.image.resnet101v2 import ResNet101V2
 from aucmedi.neural_network.architectures.image.resnet152v2 import ResNet152V2
-# ResNeXt
-from aucmedi.neural_network.architectures.image.resnext50 import ResNeXt50
-from aucmedi.neural_network.architectures.image.resnext101 import ResNeXt101
 # MobileNet
 from aucmedi.neural_network.architectures.image.mobilenet import MobileNet
 from aucmedi.neural_network.architectures.image.mobilenetv2 import MobileNetV2
@@ -86,8 +83,6 @@
     "ResNet50V2": ResNet50V2,
     "ResNet101V2": ResNet101V2,
     "ResNet152V2": ResNet152V2,
-    "ResNeXt50": ResNeXt50,
-    "ResNeXt101": ResNeXt101,
     "DenseNet121": DenseNet121,
     "DenseNet169": DenseNet169,
     "DenseNet201": DenseNet201,
@@ -153,8 +148,8 @@
 
         For example:
         ```python
-        # for the image architecture "ResNeXt101"
-        architecture="2D.ResNeXt101"
+        # for the image architecture "ResNet101"
+        architecture="2D.ResNet101"
         ```
 
     Architectures are based on the abstract base class [aucmedi.neural_network.architectures.arch_base.Architecture_Base][].
@@ -175,8 +170,6 @@
     "ResNet50V2": "tf",
     "ResNet101V2": "tf",
     "ResNet152V2": "tf",
-    "ResNeXt50": "torch",
-    "ResNeXt101": "torch",
     "DenseNet121": "torch",
     "DenseNet169": "torch",
     "DenseNet201": "torch",
@@ -237,8 +230,8 @@
 
         For example:
         ```python
-        # for the image architecture "ResNeXt101"
+        # for the image architecture "ResNet101"
         from aucmedi.neural_network.architectures import supported_standardize_mode
-        sf_norm = supported_standardize_mode["2D.ResNeXt101"]
+        sf_norm = supported_standardize_mode["2D.ResNet101"]
         ```
 """
diff --git a/aucmedi/neural_network/architectures/image/resnext101.py b/aucmedi/neural_network/architectures/image/resnext101.py
diff --git a/aucmedi/neural_network/architectures/image/resnext50.py b/aucmedi/neural_network/architectures/image/resnext50.py
diff --git a/tests/test_architectures_image.py b/tests/test_architectures_image.py
@@ -229,48 +229,6 @@ def test_ResNet152V2(self):
         self.assertTrue(supported_standardize_mode["ResNet152V2"] == "tf")
         self.assertTrue(sdm_global["2D.ResNet152V2"] == "tf")
 
-    #-------------------------------------------------#
-    #             Architecture: ResNeXt50             #
-    #-------------------------------------------------#
-    def test_ResNeXt50(self):
-        arch = ResNeXt50(Classifier(n_labels=4), channels=1,
-                                      input_shape=(32, 32))
-        model = NeuralNetwork(n_labels=4, channels=1, architecture=arch,
-                               batch_queue_size=1)
-        model.predict(self.datagen_GRAY)
-        arch = ResNeXt50(Classifier(n_labels=4), channels=3,
-                                      input_shape=(32, 32))
-        model = NeuralNetwork(n_labels=4, channels=3, architecture=arch,
-                               batch_queue_size=1)
-        model.predict(self.datagen_RGB)
-        model = NeuralNetwork(n_labels=4, channels=3, architecture="2D.ResNeXt50",
-                               batch_queue_size=1, input_shape=(32, 32))
-        try : model.model.summary()
-        except : raise Exception()
-        self.assertTrue(supported_standardize_mode["ResNeXt50"] == "torch")
-        self.assertTrue(sdm_global["2D.ResNeXt50"] == "torch")
-
-    #-------------------------------------------------#
-    #             Architecture: ResNeXt101            #
-    #-------------------------------------------------#
-    def test_ResNeXt101(self):
-        arch = ResNeXt101(Classifier(n_labels=4), channels=1,
-                                       input_shape=(32, 32))
-        model = NeuralNetwork(n_labels=4, channels=1, architecture=arch,
-                               batch_queue_size=1)
-        model.predict(self.datagen_GRAY)
-        arch = ResNeXt101(Classifier(n_labels=4), channels=3,
-                                       input_shape=(32, 32))
-        model = NeuralNetwork(n_labels=4, channels=3, architecture=arch,
-                               batch_queue_size=1)
-        model.predict(self.datagen_RGB)
-        model = NeuralNetwork(n_labels=4, channels=3, architecture="2D.ResNeXt101",
-                               batch_queue_size=1, input_shape=(32, 32))
-        try : model.model.summary()
-        except : raise Exception()
-        self.assertTrue(supported_standardize_mode["ResNeXt101"] == "torch")
-        self.assertTrue(sdm_global["2D.ResNeXt101"] == "torch")
-
     #-------------------------------------------------#
     #            Architecture: DenseNet121            #
     #-------------------------------------------------#