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ManimML/tests/test_camera_move.py
2023-02-01 22:33:42 -05:00

62 lines
2.2 KiB
Python

from manim import *
from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.layers.image import ImageLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
from PIL import Image
import numpy as np
# Make the specific scene
config.pixel_height = 1200
config.pixel_width = 1900
config.frame_height = 6.0
config.frame_width = 6.0
class NeuralNetworkScene(ThreeDScene):
"""Test Scene for the Neural Network"""
def play_camera_follow_forward_pass(self, neural_network, buffer=0.1):
per_layer_animations = neural_network.make_forward_pass_animation(
return_per_layer_animations=True
)
all_layers = neural_network.all_layers
# Compute the width and height of the frame
max_width = 0
max_height = 0
for layer_index in range(1, len(all_layers) - 1):
prev_layer = all_layers[layer_index - 1]
current_layer = all_layers[layer_index]
next_layer = all_layers[layer_index + 1]
group = Group(prev_layer, current_layer, next_layer)
max_width = max(max_width, group.width)
max_height = max(max_height, group.height)
frame_width = max_width * (1 + buffer)
frame_height = max_height * (1 + buffer)
# Go through each animation
for layer_index in range(1, len(all_layers)):
layer_animation = per_layer_animations[layer_index]
def construct(self):
# Make the Layer object
image = Image.open("../assets/mnist/digit.jpeg")
numpy_image = np.asarray(image)
nn = NeuralNetwork(
[
ImageLayer(numpy_image, height=1.5),
Convolutional2DLayer(1, 7, filter_spacing=0.32),
Convolutional2DLayer(3, 5, 3, filter_spacing=0.32),
Convolutional2DLayer(5, 3, 3, filter_spacing=0.18),
FeedForwardLayer(3),
FeedForwardLayer(3),
],
layer_spacing=0.25,
)
nn.move_to(ORIGIN)
# Make Animation
self.add(nn)
# self.play(Create(nn))
self.play_camera_follow_forward_pass(nn)