initial commit

apps/single_gradient.py

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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
+             *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
+             *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
+                 *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
+             *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"])