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R0.16.0
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,73 @@ | ||
| """ | ||
| Pytorch texture source - game of life. | ||
| """ | ||
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| import torch | ||
| import torch.nn.functional as F | ||
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| from plotoptix import TkOptiX | ||
| from plotoptix.materials import m_flat | ||
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| class params(): | ||
| if torch.cuda.is_available: | ||
| device = torch.device('cuda') | ||
| dtype = torch.float16 | ||
| else: | ||
| device = torch.device('cpu') | ||
| dtype = torch.float32 | ||
| w = torch.tensor( | ||
| [[[[1.0,1.0,1.0], [1.0,0.0,1.0], [1.0,1.0,1.0]]]], | ||
| dtype=dtype, device=device, requires_grad=False | ||
| ) | ||
| cells = torch.rand((1,1,128,128), dtype=dtype, device=device, requires_grad=False) | ||
| cells[cells > 0.995] = 1.0 | ||
| cells[cells < 1.0] = 0.0 | ||
| tex2D = torch.unsqueeze(cells[0, 0].type(torch.float32), -1).expand(-1, -1, 4).contiguous() | ||
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| # Update texture data with a simple "game of life" rules. | ||
| def compute(rt, delta): | ||
| params.cells = F.conv2d(params.cells, weight=params.w, stride=1, padding=1) | ||
| params.cells[params.cells < 2.0] = 0.0 | ||
| params.cells[params.cells > 3.0] = 0.0 | ||
| params.cells[params.cells != 0.0] = 1.0 | ||
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| # Conversion to float32 and to contiguous memoty layout is ensured by plotoptix, | ||
| # though you may wamt to make it explicit like here, eg for performance reasons. | ||
| params.tex2D = torch.unsqueeze(params.cells[0, 0].type(torch.float32), -1).expand(-1, -1, 4).contiguous() | ||
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| # Copy texture data to plotoptix scene. | ||
| def update_data(rt): | ||
| rt.set_torch_texture_2d("tex2d", params.tex2D, refresh=True) | ||
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| def main(): | ||
| rt = TkOptiX( | ||
| on_scene_compute=compute, | ||
| on_rt_completed=update_data | ||
| ) | ||
| rt.set_param(min_accumulation_step=1) | ||
| rt.set_background(0) | ||
| rt.set_ambient(0) | ||
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| # NOTE: pytorch features are not enabled by default. You need | ||
| # to call this method before using anything related to pytorch. | ||
| rt.enable_torch() | ||
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| rt.set_torch_texture_2d("tex2d", params.tex2D, addr_mode="Clamp", filter_mode="Nearest") | ||
| m_flat["ColorTextures"] = [ "tex2d" ] | ||
| rt.setup_material("flat", m_flat) | ||
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| rt.setup_camera("cam1", eye=[0, 0, 3], target=[0, 0, 0], fov=35, glock=True) | ||
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| rt.set_data("plane", geom="Parallelograms", mat="flat", | ||
| pos=[-1, -1, 0], u=[2, 0, 0], v=[0, 2, 0], c=0.9 | ||
| ) | ||
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| rt.start() | ||
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| if __name__ == '__main__': | ||
| main() | ||
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64 changes: 64 additions & 0 deletions
64
examples/2_animations_and_callbacks/6_direct_data_update.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,64 @@ | ||
| """ | ||
| Direct update of geometry data on device. | ||
| This example uses numpy arrays, but pytorch tensors are supported as well, | ||
| just note that rt.enable_torch() call is required before using pytorch. | ||
| """ | ||
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| import numpy as np | ||
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| from plotoptix import TkOptiX | ||
| from plotoptix.utils import simplex | ||
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| class params(): | ||
| n = 100 | ||
| rx = (-20, 20) | ||
| r = 0.85 * 0.5 * (rx[1] - rx[0]) / (n - 1) | ||
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| x = np.linspace(rx[0], rx[1], n) | ||
| z = np.linspace(rx[0], rx[1], n) | ||
| X, Z = np.meshgrid(x, z) | ||
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| data = np.stack([X.flatten(), np.zeros(n*n), Z.flatten()], axis=1) | ||
| t = 0 | ||
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| # Compute updated geometry data. | ||
| def compute(rt, delta): | ||
| row = np.ones((params.data.shape[0], 1)) | ||
| xn = simplex(np.concatenate([params.data, params.t * row], axis=1)) | ||
| yn = simplex(np.concatenate([params.data, (params.t + 20) * row], axis=1)) | ||
| zn = simplex(np.concatenate([params.data, (params.t - 20) * row], axis=1)) | ||
| dv = np.stack([xn, yn, zn], axis=1) | ||
| params.data += 0.02 * dv | ||
| params.t += 0.05 | ||
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| # Fast copy to geometry buffer on device, without making a host copy. | ||
| def update_data(rt): | ||
| rt.update_raw_data("balls", pos=params.data) | ||
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| def main(): | ||
| rt = TkOptiX( | ||
| on_scene_compute=compute, | ||
| on_rt_completed=update_data | ||
| ) | ||
| rt.set_param(min_accumulation_step=10, max_accumulation_frames=16) | ||
| rt.set_background(0) | ||
| rt.set_ambient(0) | ||
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| rt.setup_camera("cam1", cam_type="ThinLens", eye=[3.5, 1.27, 3.5], target=[0, 0, 0], fov=30, glock=True) | ||
| rt.setup_light("light1", pos=[4, 5, 5], color=18, radius=1.0) | ||
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| exposure = 1.0; gamma = 2.2 | ||
| rt.set_float("tonemap_exposure", exposure) | ||
| rt.set_float("tonemap_gamma", gamma) | ||
| rt.add_postproc("Gamma") | ||
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| rt.set_data("balls", pos=params.data, c=0.82, r=params.r) | ||
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| rt.start() | ||
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| if __name__ == '__main__': | ||
| main() | ||
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| Original file line number | Diff line number | Diff line change |
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@@ -12,8 +12,8 @@ | |
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| __author__ = "Robert Sulej, R&D Team <[email protected]>" | ||
| __status__ = "beta" | ||
| __version__ = "0.15.1" | ||
| __date__ = "16 March 2023" | ||
| __version__ = "0.16.0" | ||
| __date__ = "17 May 2023" | ||
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| import logging | ||
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