From d6c339dbb169583d039bf6c140ff55102eb42253 Mon Sep 17 00:00:00 2001
From: Dennis182 <Dennis.Hartmann@informatik.uni-augsburg.de>
Date: Tue, 30 Apr 2024 18:11:54 +0200
Subject: [PATCH] ResNext50 and 101 added to __init__.py of
 neural_network/architectures/image, as well as test_architectures_image.py

---
 .../architectures/image/__init__.py           |  7 +++
 tests/test_architectures_image.py             | 46 +++++++++++++++++++
 2 files changed, 53 insertions(+)

diff --git a/aucmedi/neural_network/architectures/image/__init__.py b/aucmedi/neural_network/architectures/image/__init__.py
index 6de1b5fc..6e1687d6 100644
--- a/aucmedi/neural_network/architectures/image/__init__.py
+++ b/aucmedi/neural_network/architectures/image/__init__.py
@@ -49,6 +49,9 @@
 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
@@ -83,6 +86,8 @@
     "ResNet50V2": ResNet50V2,
     "ResNet101V2": ResNet101V2,
     "ResNet152V2": ResNet152V2,
+    "ResNeXt50": ResNeXt50,
+    "ResNeXt101": ResNeXt101,
     "DenseNet121": DenseNet121,
     "DenseNet169": DenseNet169,
     "DenseNet201": DenseNet201,
@@ -170,6 +175,8 @@
     "ResNet50V2": "tf",
     "ResNet101V2": "tf",
     "ResNet152V2": "tf",
+    "ResNeXt50": "torch",
+    "ResNeXt101": "torch",
     "DenseNet121": "torch",
     "DenseNet169": "torch",
     "DenseNet201": "torch",
diff --git a/tests/test_architectures_image.py b/tests/test_architectures_image.py
index 64a1a5df..f2336460 100644
--- a/tests/test_architectures_image.py
+++ b/tests/test_architectures_image.py
@@ -229,6 +229,52 @@ 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            #
     #-------------------------------------------------#