initial commit

apps/test_eval_positions.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 = 256
+canvas_height = 256
+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 = torch.tensor([0.3, 0.6, 0.3, 1.0]))
+shape_groups = [circle_group]
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups,
+    output_type = pydiffvg.OutputType.sdf)
+
+render = pydiffvg.RenderFunction.apply
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             0,   # seed
+             *scene_args)
+img = img / 256 # Normalize SDF to [0, 1]
+pydiffvg.imwrite(img.cpu(), 'results/test_eval_positions/target.png')
+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)
+color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True)
+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,
+    output_type = pydiffvg.OutputType.sdf)
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             1,   # seed
+             *scene_args)
+img = img / 256 # Normalize SDF to [0, 1]
+pydiffvg.imwrite(img.cpu(), 'results/test_eval_positions/init.png')
+
+# Optimize for radius & center
+optimizer = torch.optim.Adam([radius_n, center_n, color], lr=1e-2)
+# Run 200 Adam iterations.
+for t in range(200):
+    print('iteration:', t)
+    optimizer.zero_grad()
+    # Forward pass: render the image.
+    circle.radius = radius_n * 256
+    circle.center = center_n * 256
+    circle_group.fill_color = color
+    # Evaluate 1000 positions
+    eval_positions = torch.rand(1000, 2) * 256
+    # for grid_sample()
+    grid_eval_positions = (eval_positions / 256.0) * 2.0 - 1.0
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups,
+        output_type = pydiffvg.OutputType.sdf,
+        eval_positions = eval_positions)
+    samples = render(256,   # width
+                     256,   # height
+                     0,     # num_samples_x
+                     0,     # num_samples_y
+                     t+1,   # seed
+                     *scene_args)
+    samples = samples / 256 # Normalize SDF to [0, 1]
+    target_sampled = torch.nn.functional.grid_sample(\
+        target.view(1, 1, target.shape[0], target.shape[1]),
+        grid_eval_positions.view(1, -1, 1, 2), mode='nearest')
+    loss = (samples - target_sampled).pow(2).mean()
+    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('color.grad:', color.grad)
+
+    # Take a gradient descent step.
+    optimizer.step()
+    # Print the current params.
+    print('radius:', circle.radius)
+    print('center:', circle.center)
+    print('color:', circle_group.fill_color)
+
+# Render the final result.
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups,
+    output_type = pydiffvg.OutputType.sdf)
+img = render(256,   # width
+             256,   # height
+             2,     # num_samples_x
+             2,     # num_samples_y
+             102,    # seed
+             *scene_args)
+img = img / 256 # Normalize SDF to [0, 1]
+# Save the images and differences.
+pydiffvg.imwrite(img.cpu(), 'results/test_eval_positions/final.png')