mirror of
https://github.com/helblazer811/ManimML.git
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245ce9424b
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.
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
from manim import *
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from manim_ml.neural_network.layers.parent_layers import ThreeDLayer, VGroupNeuralNetworkLayer
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from manim_ml.gridded_rectangle import GriddedRectangle
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import numpy as np
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class Convolutional3DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
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"""Handles rendering a convolutional layer for a nn"""
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def __init__(self, num_feature_maps, feature_map_width, feature_map_height,
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filter_width, filter_height, cell_width=0.2, filter_spacing=0.1, color=BLUE,
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pulse_color=ORANGE, filter_color=ORANGE, stride=1, stroke_width=2.0, **kwargs):
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super().__init__(**kwargs)
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self.num_feature_maps = num_feature_maps
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self.feature_map_height = feature_map_height
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self.filter_color = filter_color
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self.feature_map_width = feature_map_width
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self.filter_width = filter_width
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self.filter_height = filter_height
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self.cell_width = cell_width
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self.filter_spacing = filter_spacing
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self.color = color
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self.pulse_color = pulse_color
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self.stride = stride
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self.stroke_width = stroke_width
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# Make the feature maps
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self.feature_maps = self.construct_feature_maps()
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self.add(self.feature_maps)
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# Rotate stuff properly
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self.rotate(
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ThreeDLayer.three_d_x_rotation,
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about_point=self.get_center(),
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axis=[1, 0, 0]
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)
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self.rotate(
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ThreeDLayer.three_d_y_rotation,
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about_point=self.get_center(),
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axis=[0, 1, 0]
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)
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def construct_feature_maps(self):
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"""Creates the neural network layer"""
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# Draw rectangles that are filled in with opacity
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feature_maps = []
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for filter_index in range(self.num_feature_maps):
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rectangle = GriddedRectangle(
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color=self.color,
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height=self.feature_map_height * self.cell_width,
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width=self.feature_map_width * self.cell_width,
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fill_color=self.color,
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fill_opacity=0.2,
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stroke_color=self.color,
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stroke_width=self.stroke_width,
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# grid_xstep=self.cell_width,
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# grid_ystep=self.cell_width,
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# grid_stroke_width=DEFAULT_STROKE_WIDTH/2
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)
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# Move the feature map
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rectangle.move_to(
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[0, 0, filter_index * self.filter_spacing]
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)
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feature_maps.append(rectangle)
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return VGroup(*feature_maps)
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def make_forward_pass_animation(
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self,
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run_time=5,
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corner_pulses=False,
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layer_args={},
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**kwargs
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):
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"""Convolution forward pass animation"""
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# Note: most of this animation is done in the Convolution3DToConvolution3D layer
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print(f"Corner pulses: {corner_pulses}")
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if corner_pulses:
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raise NotImplementedError()
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passing_flashes = []
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for line in self.corner_lines:
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pulse = ShowPassingFlash(
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line.copy()
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.set_color(self.pulse_color)
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.set_stroke(opacity=1.0),
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time_width=0.5,
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run_time=run_time,
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rate_func=rate_functions.linear
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)
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passing_flashes.append(pulse)
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# per_filter_run_time = run_time / len(self.feature_maps)
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# Make animation group
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animation_group = AnimationGroup(
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*passing_flashes,
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# filter_flashes
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)
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else:
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animation_group = AnimationGroup()
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return animation_group
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def scale(self, scale_factor, **kwargs):
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self.cell_width *= scale_factor
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super().scale(scale_factor, **kwargs)
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@override_animation(Create)
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def _create_override(self, **kwargs):
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return FadeIn(self.feature_maps)
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