ayo
7c5d3c9157
Add native K/M/RR inference engines, HIP kernels, replay and validation harnesses, and Windows DLSS setup and runtime support. Include GPU profiling tools, build and packaging scripts, platform integration patches, and implementation documentation.
61 lines
3.3 KiB
Python
61 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""Run ordinary linear HDR frames through the native engine without an NGX capture.
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prepare_frames.py FRAMES.json CASE.txt
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JSON has output: [width,height] and frames: [{color, motion, depth, render: [w,h],
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bits: 16, color_exposure: 1, history_exposure_ratio: 1, network_exposure_scale: 1,
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jitter: [0,0], motion_scale: [0,0], motion_offset: [0,0], sharpness: 1.37, reset: true}].
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Paths are relative to the JSON. Color is interleaved RGBA16F/32F, motion RG16F/32F,
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depth R32F (inverse depth). Subsequent frames keep the engine's own history.
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The first frame resets. Attention windows default to a fixed four-token shift;
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captured NGX window scheduling is available separately through prepare_k.py.
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"""
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import argparse
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import json
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import math
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from pathlib import Path
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def main():
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument('frames', type=Path)
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p.add_argument('output', type=Path)
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a = p.parse_args()
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config = json.loads(a.frames.read_text())
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ow, oh = config['output']
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if not all(isinstance(x, int) and 256 <= x <= 8192 for x in (ow,oh)):
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raise ValueError('output dimensions must be integers in 256..8192')
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frames = config['frames']
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if not frames: raise ValueError('no frames')
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lines = [f'output {ow} {oh}']
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for i, frame in enumerate(frames):
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rw, rh = frame['render']; bits = frame.get('bits',16)
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if not isinstance(rw,int) or not isinstance(rh,int) or not 0 < rw <= ow or not 0 < rh <= oh or bits not in (16,32):
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raise ValueError('invalid render dimensions or component size')
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for j, (key, channels, b) in enumerate((('color',4,bits),('motion',2,bits),('depth',1,32))):
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path = (a.frames.resolve().parent / frame[key]).resolve()
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if path.stat().st_size != rw * rh * channels * b // 8:
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raise ValueError(f'{path}: unexpected frame image size')
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lines.append(f'image {i*3+j} {json.dumps(str(path))} {rw} {rh} {channels} {b}')
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reset = frame.get('reset', i == 0)
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if not isinstance(reset,bool): raise ValueError('reset must be a boolean')
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if i == 0 and not reset: raise ValueError('the first ordinary frame must reset')
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exposure = frame.get('color_exposure',1)
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ratio = frame.get('history_exposure_ratio',1)
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network = frame.get('network_exposure_scale',1)
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if not all(math.isfinite(x) and x > 0 for x in (exposure,ratio,network)):
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raise ValueError('exposure factors must be finite and positive')
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vectors = [frame.get(k,[0,0]) for k in ('jitter','motion_scale','motion_offset')]
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if any(len(v) != 2 or not all(math.isfinite(x) for x in v) for v in vectors):
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raise ValueError('jitter and motion vectors must contain two finite components')
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sharpness = frame.get('sharpness',1.37)
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if not math.isfinite(sharpness): raise ValueError('sharpness must be finite')
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values = [i*3,i*3+1,i*3+2,exposure,ratio,network,int(reset),*vectors[0],*vectors[1],*vectors[2],sharpness]
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lines.append('frame_auto ' + ' '.join(map(str,values)))
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a.output.parent.mkdir(parents=True,exist_ok=True)
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a.output.write_text('\n'.join(lines)+'\n')
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print(f'Prepared {len(frames)} ordinary frames for {ow}x{oh} native inference')
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if __name__ == '__main__': main()
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