Merge branch 'main' into 139-missing-tffunction-parameters

Author: Tatiana Castro-Vélez

edu.cuny.hunter.hybridize.core/META-INF/MANIFEST.MF

@@ -11,11 +11,11 @@ Require-Bundle: org.eclipse.ltk.core.refactoring;bundle-version="3.12.200",
  edu.cuny.citytech.refactoring.common.core;bundle-version="3.1.0",
  org.eclipse.equinox.registry;bundle-version="3.11.100",
  org.eclipse.osgi;bundle-version="3.18.0",
- org.python.pydev.parser;bundle-version="9.3.2",
- org.python.pydev.core;bundle-version="9.3.2",
- org.python.pydev.ast;bundle-version="9.3.2",
- com.python.pydev.refactoring,
- com.python.pydev.analysis,
+ org.python.pydev.parser;bundle-version="[9.3.2,10.0.0)",
+ org.python.pydev.core;bundle-version="[9.3.2,10.0.0)",
+ org.python.pydev.ast;bundle-version="[9.3.2,10.0.0)",
+ com.python.pydev.refactoring;bundle-version="[9.3.2,10.0.0)",
+ com.python.pydev.analysis;bundle-version="[9.3.2,10.0.0)",
  org.eclipse.core.resources;bundle-version="3.18.0"
 Import-Package: org.eclipse.core.runtime;version="3.7.0",
  org.eclipse.jface.text,

edu.cuny.hunter.hybridize.tests/META-INF/MANIFEST.MF

@@ -9,17 +9,17 @@ Automatic-Module-Name: edu.cuny.hunter.hybridize.tests
 Bundle-RequiredExecutionEnvironment: JavaSE-11
 Require-Bundle: edu.cuny.citytech.refactoring.common.tests;bundle-version="3.1.0",
  org.eclipse.jdt.ui.tests.refactoring;bundle-version="3.14.300",
- org.python.pydev;bundle-version="9.3.2",
+ org.python.pydev;bundle-version="[9.3.2,10.0.0)",
  org.junit;bundle-version="4.13.2",
  org.eclipse.ltk.core.refactoring;bundle-version="3.12.200",
  org.eclipse.jface.text;bundle-version="3.20.100",
  org.eclipse.core.runtime;bundle-version="3.25.0",
- org.python.pydev.parser;bundle-version="9.3.2",
- org.python.pydev.core;bundle-version="9.3.2",
+ org.python.pydev.parser;bundle-version="[9.3.2,10.0.0)",
+ org.python.pydev.core;bundle-version="[9.3.2,10.0.0)",
  edu.cuny.hunter.hybridize.core;bundle-version="1.0.0",
  edu.cuny.citytech.refactoring.common.core;bundle-version="3.0.0",
- org.python.pydev.ast,
- org.python.pydev.refactoring
+ org.python.pydev.ast;bundle-version="[9.3.2,10.0.0)",
+ org.python.pydev.refactoring;bundle-version="[9.3.2,10.0.0)"
 Export-Package: edu.cuny.hunter.hybridize.tests
 Import-Package: com.python.pydev.analysis.additionalinfo,
  org.eclipse.core.resources,

edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel/in/A.py

@@ -16,7 +16,7 @@ def __init__(self, **kwargs):
     self.dropout = tf.keras.layers.Dropout(0.2)
     self.dense_2 = tf.keras.layers.Dense(10)
 
-  def call(self, x):
+  def __call__(self, x):
     x = self.flatten(x)
 
     for layer in self.my_layers:

edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel2/in/A.py

@@ -0,0 +1,41 @@
+import tensorflow as tf
+
+# Create an override model to classify pictures
+
+class SequentialModel(tf.keras.Model):
+  def __init__(self, **kwargs):
+    super(SequentialModel, self).__init__(**kwargs)
+
+    self.flatten = tf.keras.layers.Flatten(input_shape=(28, 28))
+
+    # Add a lot of small layers
+    num_layers = 100
+    self.my_layers = [tf.keras.layers.Dense(64, activation="relu")
+                      for n in range(num_layers)]
+
+    self.dropout = tf.keras.layers.Dropout(0.2)
+    self.dense_2 = tf.keras.layers.Dense(10)
+
+  def call(self, x):
+    x = self.flatten(x)
+
+    for layer in self.my_layers:
+      x = layer(x)
+
+    x = self.dropout(x)
+    x = self.dense_2(x)
+
+    return x
+
+if __name__ == '__main__':
+    input_data = tf.random.uniform([20, 28, 28])
+    print("Input:")
+    print(type(input_data))
+    print(input_data)
+
+    model = SequentialModel()
+    result = model(input_data)
+
+    print("Output:")
+    print(type(input_data))
+    print(result)

edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel2/in/requirements.txt

@@ -0,0 +1 @@
+tensorflow==2.9.3

edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel3/in/A.py

@@ -0,0 +1,41 @@
+import tensorflow as tf
+
+# Create an override model to classify pictures
+
+class SequentialModel(tf.keras.Model):
+  def __init__(self, **kwargs):
+    super(SequentialModel, self).__init__(**kwargs)
+
+    self.flatten = tf.keras.layers.Flatten(input_shape=(28, 28))
+
+    # Add a lot of small layers
+    num_layers = 100
+    self.my_layers = [tf.keras.layers.Dense(64, activation="relu")
+                      for n in range(num_layers)]
+
+    self.dropout = tf.keras.layers.Dropout(0.2)
+    self.dense_2 = tf.keras.layers.Dense(10)
+
+  def call(self, x):
+    x = self.flatten(x)
+
+    for layer in self.my_layers:
+      x = layer(x)
+
+    x = self.dropout(x)
+    x = self.dense_2(x)
+
+    return x
+
+if __name__ == '__main__':
+    input_data = tf.random.uniform([20, 28, 28])
+    print("Input:")
+    print(type(input_data))
+    print(input_data)
+
+    model = SequentialModel()
+    result = model.call(input_data)
+
+    print("Output:")
+    print(type(input_data))
+    print(result)

edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel3/in/requirements.txt

@@ -0,0 +1 @@
+tensorflow==2.9.3

edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel4/in/A.py

@@ -0,0 +1,41 @@
+import tensorflow as tf
+
+# Create an override model to classify pictures
+
+class SequentialModel(tf.keras.Model):
+  def __init__(self, **kwargs):
+    super(SequentialModel, self).__init__(**kwargs)
+
+    self.flatten = tf.keras.layers.Flatten(input_shape=(28, 28))
+
+    # Add a lot of small layers
+    num_layers = 100
+    self.my_layers = [tf.keras.layers.Dense(64, activation="relu")
+                      for n in range(num_layers)]
+
+    self.dropout = tf.keras.layers.Dropout(0.2)
+    self.dense_2 = tf.keras.layers.Dense(10)
+
+  def __call__(self, x):
+    x = self.flatten(x)
+
+    for layer in self.my_layers:
+      x = layer(x)
+
+    x = self.dropout(x)
+    x = self.dense_2(x)
+
+    return x
+
+if __name__ == '__main__':
+    input_data = tf.random.uniform([20, 28, 28])
+    print("Input:")
+    print(type(input_data))
+    print(input_data)
+
+    model = SequentialModel()
+    result = model.__call__(input_data)
+
+    print("Output:")
+    print(type(input_data))
+    print(result)

edu.cuny.hunter.hybridize.tests/resources/HybridizeFunction/testModel4/in/requirements.txt

@@ -0,0 +1 @@
+tensorflow==2.9.3

edu.cuny.hunter.hybridize.tests/test cases/edu/cuny/hunter/hybridize/tests/HybridizeFunctionRefactoringTest.java

@@ -1619,4 +1619,49 @@ public void testModel() throws Exception {
 		// no hybrids.
 		assertTrue(functions.stream().map(Function::isHybrid).allMatch(b -> b == false));
 	}
+
+	/**
+	 * Test a model. No tf.function in this one. Use call instead of __call__. Ariadne doesn't support __call__.
+	 * See https://github.com/wala/ML/issues/24.
+	 */
+	@Test
+	public void testModel2() throws Exception {
+		Set<Function> functions = this.getFunctions();
+		assertNotNull(functions);
+
+		LOG.info("Found functions: " + functions.size());
+
+		// no hybrids.
+		assertTrue(functions.stream().map(Function::isHybrid).allMatch(b -> b == false));
+	}
+
+	/**
+	 * Test a model. No tf.function in this one. Explicit call method.
+	 */
+	@Test
+	public void testModel3() throws Exception {
+		Set<Function> functions = this.getFunctions();
+		assertNotNull(functions);
+
+		LOG.info("Found functions: " + functions.size());
+
+		// no hybrids.
+		assertTrue(functions.stream().map(Function::isHybrid).allMatch(b -> b == false));
+	}
+
+	/**
+	 * Test a model. No tf.function in this one. Explicit call method.
+	 */
+	@Test
+	public void testModel4() throws Exception {
+		Set<Function> functions = this.getFunctions();
+		assertNotNull(functions);
+
+		LOG.info("Found functions: " + functions.size());
+
+		// no hybrids.
+		assertTrue(functions.stream().map(Function::isHybrid).allMatch(b -> b == false));
+	}
+
+	// TODO: Test models that have tf.functions.
 }