Made a workaround to make sure the filters in the CNN 2 CNN layers

don't appear at the beggining of a forward pass animation. I think
this has to do with a bug in Succession in the core ManimCommunity
library.

manim_ml/decision_tree/decision_tree.py

@@ -8,7 +8,7 @@ from manim_ml.one_to_one_sync import OneToOneSync
 class LeafNode(VGroup):
     pass
 
-class SplitNode(VGroup):
+class NonLeafNode(VGroup):
     pass
 
 class DecisionTreeDiagram(Graph):
@@ -22,5 +22,17 @@ class DecisionTreeEmbedding():
 class DecisionTreeContainer(OneToOneSync):
     """Connects the DecisionTreeDiagram to the DecisionTreeEmbedding"""
 
-    def __init__(self):
+    def __init__(self, sklearn_tree, points, classes):
+        self.sklearn_tree = sklearn_tree
+        self.points = points
+        self.classes = classes
+
+    def make_unfold_tree_animation(self):
+        """Unfolds the tree through an in order traversal
+
+        This animations unfolds the tree diagram as well as showing the splitting
+        of a shaded region in the Decision Tree embedding.
+        """
+        # Draw points in the embedding
+        # Start the tree splitting animation
         pass

manim_ml/flow/flow.py

@@ -0,0 +1,17 @@
+"""
+    Animated flow charts. 
+"""
+from manim import *
+
+class FlowGraph(VGroup):
+    """Graph container"""
+    pass
+
+class FlowNode(VGroup):
+    """Node in the FlowGraph"""
+    pass
+
+class DataNode(FlowNode):
+    """Node that outputs data"""
+    pass
+

manim_ml/neural_network/layers/convolutional3d.py

@@ -40,7 +40,7 @@ class Convolutional3DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
     def construct_feature_maps(self):
         """Creates the neural network layer"""
         # Draw rectangles that are filled in with opacity
-        feature_maps = VGroup()
+        feature_maps = []
         for filter_index in range(self.num_feature_maps):
             rectangle = GriddedRectangle(
                 color=self.color, 
@@ -54,19 +54,13 @@ class Convolutional3DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
                 # grid_ystep=self.cell_width,
                 # grid_stroke_width=DEFAULT_STROKE_WIDTH/2
             )
+            # Move the feature map
             rectangle.move_to(
                 [0, 0, filter_index * self.filter_spacing]
             )
-            # Rotate about z axis
+            feature_maps.append(rectangle)
-            """
+
-            rectangle.rotate_about_origin(
+        return VGroup(*feature_maps)
-                90 * DEGREES, 
-                np.array([0, 1, 0])
-            ) 
-            """
-            feature_maps.add(rectangle)
-        
-        return feature_maps
 
     def make_forward_pass_animation(
             self, 
@@ -79,6 +73,7 @@ class Convolutional3DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
         # Note: most of this animation is done in the Convolution3DToConvolution3D layer
         print(f"Corner pulses: {corner_pulses}")
         if corner_pulses:
+            raise NotImplementedError()
             passing_flashes = []
             for line in self.corner_lines:
                 pulse = ShowPassingFlash(

manim_ml/neural_network/layers/convolutional3d_to_convolutional3d.py

@@ -22,11 +22,11 @@ class Filters(VGroup):
         self.stroke_width = stroke_width
         # Make the filter
         self.input_rectangles = self.make_input_feature_map_rectangles()
-        self.add(self.input_rectangles)
+        # self.add(self.input_rectangles)
         self.output_rectangles = self.make_output_feature_map_rectangles()
-        self.add(self.output_rectangles)
+        # self.add(self.output_rectangles)
         self.connective_lines = self.make_connective_lines()
-        self.add(self.connective_lines)
+        # self.add(self.connective_lines)
 
     def make_input_feature_map_rectangles(self):
         rectangles = []
@@ -185,6 +185,34 @@ class Filters(VGroup):
  
         return connective_lines
 
+    @override_animation(Create)
+    def _create_override(self, **kwargs):
+        """
+            NOTE This create override animation
+            is a workaround to make sure that the filter
+            does not show up in the scene before the create animation. 
+
+            Without this override the filters were shown at the beginning
+            of the neural network forward pass animimation 
+            instead of just when the filters were supposed to appear. 
+            I think this is a bug with Succession in the core 
+            Manim Community Library. 
+
+            TODO Fix this 
+        """
+        def add_content(object):
+            object.add(self.input_rectangles) 
+            object.add(self.connective_lines) 
+            object.add(self.output_rectangles) 
+
+            return object
+
+        return ApplyFunction(
+            add_content,
+            self
+        )
+
+
 class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
     """Feed Forward to Embedding Layer"""
     input_class = Convolutional3DLayer
@@ -208,9 +236,6 @@ class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
         self.filter_opacity = filter_opacity
         self.line_color = line_color
         self.pulse_color = pulse_color
-        # Make filters
-        self.filters = Filters(self.input_layer, self.output_layer)
-        self.add(self.filters)
 
     def make_filter_propagation_animation(self):
         """Make filter propagation animation"""
@@ -251,26 +276,17 @@ class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
 
         return right_shift, down_shift
 
-    def make_forward_pass_animation(self, layer_args={}, run_time=10.5, **kwargs):
+    def make_forward_pass_animation(self, layer_args={}, 
+            all_filters_at_once=False, run_time=10.5, **kwargs):
         """Forward pass animation from conv2d to conv2d"""
-
         animations = []
-        # Rotate given three_d_phi and three_d_theta
+        # Make filters
-        # Rotate about center
+        filters = Filters(self.input_layer, self.output_layer)
-        # filters.rotate(110 * DEGREES, about_point=filters.get_center(), axis=[0, 0, 1])
+        filters.set_z_index(self.input_layer.feature_maps[0].get_z_index() + 1)
-        """
+        # self.add(filters)
-        self.filters.rotate(
+        animations.append(
-            ThreeDLayer.three_d_x_rotation, 
+            Create(filters)
-            about_point=self.filters.get_center(), 
-            axis=[1, 0, 0]
         )
-
-        self.filters.rotate(
-            ThreeDLayer.three_d_y_rotation, 
-            about_point=self.filters.get_center(), 
-            axis=[0, 1, 0]
-        )
-        """
         # Get shift vectors
         right_shift, down_shift = self.get_rotated_shift_vectors()
         left_shift = -1 * right_shift
@@ -279,9 +295,6 @@ class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
         # Make animations for creating the filters, output_nodes, and filter_lines
         # TODO decide if I want to create the filters at the start of a conv 
         # animation or have them there by default
-        # animations.append(
-        #     Create(filters)
-        # )
         # Rotate the base shift vectors
         # Make filter shifting animations
         num_y_moves = int((self.feature_map_height - self.filter_height) / self.stride)
@@ -289,10 +302,9 @@ class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
         for y_move in range(num_y_moves):
             # Go right num_x_moves
             for x_move in range(num_x_moves):
-                print(right_shift)
                 # Shift right
                 shift_animation = ApplyMethod(
-                    self.filters.shift,
+                    filters.shift,
                     self.stride * right_shift
                 )
                 # shift_animation = self.animate.shift(right_shift)
@@ -302,7 +314,7 @@ class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
             shift_amount = self.stride * num_x_moves * left_shift + self.stride * down_shift
             # Make the animation
             shift_animation = ApplyMethod(
-                self.filters.shift,
+                filters.shift,
                 shift_amount  
             )
             animations.append(shift_animation)
@@ -310,12 +322,15 @@ class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
         for x_move in range(num_x_moves):
             # Shift right
             shift_animation = ApplyMethod(
-                self.filters.shift,
+                filters.shift,
                 self.stride * right_shift
             )
             # shift_animation = self.animate.shift(right_shift)
             animations.append(shift_animation)
-            
+        # Remove the filters
+        animations.append(
+            FadeOut(filters)
+        )
         # Remove filters
         return Succession(
             *animations,
@@ -332,4 +347,4 @@ class Convolutional3DToConvolutional3D(ConnectiveLayer, ThreeDLayer):
 
     @override_animation(Create)
     def _create_override(self, **kwargs):
-        return AnimationGroup()
+        return Succession()

manim_ml/neural_network/layers/image.py

@@ -43,7 +43,7 @@ class ImageLayer(NeuralNetworkLayer):
             return AnimationGroup()
 
     def make_forward_pass_animation(self, layer_args={}, **kwargs):
-        return FadeIn(self.image_mobject)
+        return AnimationGroup()
     
     # def move_to(self, location):
     #     """Override of move to"""

tests/test_3d_camera_move.py

@@ -19,26 +19,28 @@ class CombinedScene(ThreeDScene):
         # Make nn
         nn = NeuralNetwork(
             [
-                ImageLayer(numpy_image, height=1.4),
+                ImageLayer(numpy_image, height=2.0),
-                Convolutional3DLayer(1, 7, 7, 3, 3, filter_spacing=0.2),
+                Convolutional3DLayer(1, 7, 7, 3, 3, filter_spacing=0.32),
-                Convolutional3DLayer(3, 5, 5, 3, 3, filter_spacing=0.2),
+                Convolutional3DLayer(3, 5, 5, 3, 3, filter_spacing=0.32),
-                Convolutional3DLayer(5, 3, 3, 1, 1, filter_spacing=0.2),
+                Convolutional3DLayer(5, 3, 3, 1, 1, filter_spacing=0.18),
-                FeedForwardLayer(3, rectangle_stroke_width=4, node_stroke_width=4),
+                FeedForwardLayer(3),
-                FeedForwardLayer(3, rectangle_stroke_width=4, node_stroke_width=4),
+                FeedForwardLayer(3),
             ], 
-            layer_spacing=0.5,
+            layer_spacing=0.25,
             # camera=self.camera
         )
         # Center the nn
-        self.add(nn)
+        # self.add(nn)
         nn.move_to(ORIGIN)
+        self.play(
+            FadeIn(nn)
+        )
         # Play animation
         forward_pass = nn.make_forward_pass_animation(
             corner_pulses=False,
-            layer_args={
+            all_filters_at_once=True
-                "all_filters_at_once": True
-            }
         )
+        self.wait(1)
         self.play(
             forward_pass
         )