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124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
"""This module is dedicated to visualizing VAE disentanglement"""
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from pathlib import Path
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from manim import *
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from manim_ml.neural_network.layers import FeedForwardLayer
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from manim_ml.neural_network.neural_network import NeuralNetwork
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import pickle
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ROOT_DIR = Path(__file__).parents[2]
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def construct_image_mobject(input_image, height=2.3):
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"""Constructs an ImageMobject from a numpy grayscale image"""
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# Convert image to rgb
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if len(input_image.shape) == 2:
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input_image = np.repeat(input_image, 3, axis=0)
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input_image = np.rollaxis(input_image, 0, start=3)
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# Make the ImageMobject
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image_mobject = ImageMobject(input_image, image_mode="RGB")
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image_mobject.set_resampling_algorithm(RESAMPLING_ALGORITHMS["nearest"])
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image_mobject.height = height
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return image_mobject
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class DisentanglementVisualization(VGroup):
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def __init__(
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self,
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model_path=ROOT_DIR
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/ "examples/variational_autoencoder/autoencoder_models/saved_models/model_dim2.pth",
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image_height=0.35,
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):
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self.model_path = model_path
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self.image_height = image_height
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# Load disentanglement image objects
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with open(
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ROOT_DIR
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/ "examples/variational_autoencoder/autoencoder_models/disentanglement.pkl",
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"rb",
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) as f:
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self.image_handler = pickle.load(f)
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def make_disentanglement_generation_animation(self):
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animation_list = []
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for image_index, image in enumerate(self.image_handler["images"]):
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image_mobject = construct_image_mobject(image, height=self.image_height)
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r, c = self.image_handler["bin_indices"][image_index]
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# Move the image to the correct location
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r_offset = -1.2
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c_offset = 0.25
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image_location = [
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c_offset + c * self.image_height,
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r_offset + r * self.image_height,
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0,
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]
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image_mobject.move_to(image_location)
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animation_list.append(FadeIn(image_mobject))
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generation_animation = AnimationGroup(*animation_list[::-1], lag_ratio=1.0)
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return generation_animation
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config.pixel_height = 720
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config.pixel_width = 1280
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config.frame_height = 5.0
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config.frame_width = 5.0
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class DisentanglementScene(Scene):
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"""Disentanglement Scene Object"""
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def _construct_embedding(self, point_color=BLUE, dot_radius=0.05):
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"""Makes a Gaussian-like embedding"""
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embedding = VGroup()
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# Sample points from a Gaussian
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num_points = 200
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standard_deviation = [0.6, 0.8]
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mean = [0, 0]
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points = np.random.normal(mean, standard_deviation, size=(num_points, 2))
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# Make an axes
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embedding.axes = Axes(
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x_range=[-3, 3],
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y_range=[-3, 3],
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x_length=2.2,
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y_length=2.2,
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tips=False,
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)
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# Add each point to the axes
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self.point_dots = VGroup()
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for point in points:
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point_location = embedding.axes.coords_to_point(*point)
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dot = Dot(point_location, color=point_color, radius=dot_radius / 2)
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self.point_dots.add(dot)
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embedding.add(self.point_dots)
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return embedding
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def construct(self):
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# Make the VAE decoder
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vae_decoder = NeuralNetwork(
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[
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FeedForwardLayer(3),
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FeedForwardLayer(5),
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],
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layer_spacing=0.55,
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)
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vae_decoder.shift([-0.55, 0, 0])
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self.play(Create(vae_decoder), run_time=1)
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# Make the embedding
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embedding = self._construct_embedding()
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embedding.scale(0.9)
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embedding.move_to(vae_decoder.get_left())
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embedding.shift([-0.85, 0, 0])
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self.play(Create(embedding))
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# Make disentanglment visualization
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disentanglement = DisentanglementVisualization()
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disentanglement_animation = (
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disentanglement.make_disentanglement_generation_animation()
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)
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self.play(disentanglement_animation, run_time=3)
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self.play(Wait(2))
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