103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
import pydiffvg
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import torch
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import skimage
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import numpy as np
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# Use GPU if available
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pydiffvg.set_use_gpu(torch.cuda.is_available())
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canvas_width, canvas_height = 256 ,256
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rect = pydiffvg.Rect(p_min = torch.tensor([40.0, 40.0]),
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p_max = torch.tensor([160.0, 160.0]))
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shapes = [rect]
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rect_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
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fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
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shape_groups = [rect_group]
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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render = pydiffvg.RenderFunction.apply
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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0, # seed
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*scene_args)
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# The output image is in linear RGB space. Do Gamma correction before saving the image.
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pydiffvg.imwrite(img.cpu(), 'results/single_rect/target.png', gamma=2.2)
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target = img.clone()
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# Move the rect to produce initial guess
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# normalize p_min & p_max for easier learning rate
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p_min_n = torch.tensor([80.0 / 256.0, 20.0 / 256.0], requires_grad=True)
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p_max_n = torch.tensor([100.0 / 256.0, 60.0 / 256.0], requires_grad=True)
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color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
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rect.p_min = p_min_n * 256
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rect.p_max = p_max_n * 256
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rect_group.fill_color = color
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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1, # seed
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*scene_args)
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pydiffvg.imwrite(img.cpu(), 'results/single_rect/init.png', gamma=2.2)
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# Optimize for radius & center
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optimizer = torch.optim.Adam([p_min_n, p_max_n, color], lr=1e-2)
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# Run 100 Adam iterations.
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for t in range(100):
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print('iteration:', t)
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optimizer.zero_grad()
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# Forward pass: render the image.
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rect.p_min = p_min_n * 256
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rect.p_max = p_max_n * 256
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rect_group.fill_color = color
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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t+1, # seed
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*scene_args)
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# Save the intermediate render.
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pydiffvg.imwrite(img.cpu(), 'results/single_rect/iter_{}.png'.format(t), gamma=2.2)
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# Compute the loss function. Here it is L2.
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loss = (img - target).pow(2).sum()
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print('loss:', loss.item())
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# Backpropagate the gradients.
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loss.backward()
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# Print the gradients
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print('p_min.grad:', p_min_n.grad)
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print('p_max.grad:', p_max_n.grad)
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print('color.grad:', color.grad)
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# Take a gradient descent step.
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optimizer.step()
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# Print the current params.
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print('p_min:', rect.p_min)
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print('p_max:', rect.p_max)
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print('color:', rect_group.fill_color)
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# Render the final result.
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups)
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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102, # seed
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*scene_args)
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# Save the images and differences.
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pydiffvg.imwrite(img.cpu(), 'results/single_rect/final.png')
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# Convert the intermediate renderings to a video.
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from subprocess import call
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call(["ffmpeg", "-framerate", "24", "-i",
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"results/single_rect/iter_%d.png", "-vb", "20M",
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"results/single_rect/out.mp4"])
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