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import pydiffvg
import torch
import skimage
import numpy as np
# Use GPU if available
pydiffvg.set_use_gpu(torch.cuda.is_available())
canvas_width, canvas_height = 256, 256
color = pydiffvg.LinearGradient(\
begin = torch.tensor([50.0, 50.0]),
end = torch.tensor([200.0, 200.0]),
offsets = torch.tensor([0.0, 1.0]),
stop_colors = torch.tensor([[0.2, 0.5, 0.7, 1.0],
[0.7, 0.2, 0.5, 1.0]]))
circle = pydiffvg.Circle(radius = torch.tensor(40.0),
center = torch.tensor([128.0, 128.0]))
shapes = [circle]
circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), fill_color = color)
shape_groups = [circle_group]
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
render = pydiffvg.RenderFunction.apply
img = render(256, # width
256, # height
2, # num_samples_x
2, # num_samples_y
0, # seed
None, # background_image
*scene_args)
# The output image is in linear RGB space. Do Gamma correction before saving the image.
pydiffvg.imwrite(img.cpu(), 'results/single_gradient/target.png', gamma=2.2)
target = img.clone()
# Move the circle to produce initial guess
# normalize radius & center for easier learning rate
radius_n = torch.tensor(20.0 / 256.0, requires_grad=True)
center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True)
begin_n = torch.tensor([100.0 / 256.0, 100.0 / 256.0], requires_grad=True)
end_n = torch.tensor([150.0 / 256.0, 150.0 / 256.0], requires_grad=True)
stop_colors = torch.tensor([[0.1, 0.9, 0.2, 1.0],
[0.5, 0.3, 0.6, 1.0]], requires_grad=True)
color.begin = begin_n * 256
color.end = end_n * 256
color.stop_colors = stop_colors
circle.radius = radius_n * 256
circle.center = center_n * 256
circle_group.fill_color = color
shapes = [circle]
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(256, # width
256, # height
2, # num_samples_x
2, # num_samples_y
1, # seed
None, # background_image
*scene_args)
pydiffvg.imwrite(img.cpu(), 'results/single_gradient/init.png', gamma=2.2)
# Optimize for radius & center
optimizer = torch.optim.Adam([radius_n, center_n, begin_n, end_n, stop_colors], lr=1e-2)
# Run 50 Adam iterations.
for t in range(100):
print('iteration:', t)
optimizer.zero_grad()
# Forward pass: render the image.
color.begin = begin_n * 256
color.end = end_n * 256
color.stop_colors = stop_colors
circle.radius = radius_n * 256
circle.center = center_n * 256
circle_group.fill_color = color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(256, # width
256, # height
2, # num_samples_x
2, # num_samples_y
t+1, # seed
None, # background_image
*scene_args)
# Save the intermediate render.
pydiffvg.imwrite(img.cpu(), 'results/single_gradient/iter_{}.png'.format(t), gamma=2.2)
# Compute the loss function. Here it is L2.
loss = (img - target).pow(2).sum()
print('loss:', loss.item())
# Backpropagate the gradients.
loss.backward()
# Print the gradients
print('radius.grad:', radius_n.grad)
print('center.grad:', center_n.grad)
print('begin.grad:', begin_n.grad)
print('end.grad:', end_n.grad)
print('stop_colors.grad:', stop_colors.grad)
# Take a gradient descent step.
optimizer.step()
# Print the current params.
print('radius:', circle.radius)
print('center:', circle.center)
print('begin:', begin_n)
print('end:', end_n)
print('stop_colors:', stop_colors)
# Render the final result.
color.begin = begin_n * 256
color.end = end_n * 256
color.stop_colors = stop_colors
circle.radius = radius_n * 256
circle.center = center_n * 256
circle_group.fill_color = color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(256, # width
256, # height
2, # num_samples_x
2, # num_samples_y
52, # seed
None, # background_image
*scene_args)
# Save the images and differences.
pydiffvg.imwrite(img.cpu(), 'results/single_gradient/final.png')
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/single_gradient/iter_%d.png", "-vb", "20M",
"results/single_gradient/out.mp4"])
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