9f851e2d45
Links the local llama.cpp April build (libmtmd exports clip_* internals); generates deterministic PPM test images; dumps preprocessed f32 tensor and projected [n_tokens x 4096] embeddings. Reference stats recorded: radar 512x384 -> 192 tokens, cpu-vs-metal spread ~2e-4 abs on embd values. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
#!/usr/bin/env python3
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"""Deterministic P6 PPM test images for VLM oracle comparison (stdlib only)."""
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import struct, sys, math, os
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def write_ppm(path, w, h, pixel_fn):
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buf = bytearray()
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for y in range(h):
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for x in range(w):
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r, g, b = pixel_fn(x, y)
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buf += bytes((max(0, min(255, int(r))), max(0, min(255, int(g))), max(0, min(255, int(b)))))
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with open(path, 'wb') as f:
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f.write(b'P6\n%d %d\n255\n' % (w, h))
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f.write(buf)
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print(path, w, 'x', h)
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out_dir = os.path.dirname(os.path.abspath(__file__))
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# 1. radar-like: map-ish background, green/yellow/red rain blobs (512x384, multiple of 32)
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def radar(x, y):
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# pale map background with faint road grid
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r, g, b = 232, 236, 240
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if x % 64 < 2 or y % 64 < 2:
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r, g, b = 200, 200, 205
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# rain cells: three gaussian blobs of increasing intensity
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for (cx, cy, s, col) in [(140, 120, 55, (120, 200, 120)),
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(300, 200, 70, (240, 220, 100)),
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(330, 180, 30, (220, 80, 60))]:
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d2 = (x - cx) ** 2 + (y - cy) ** 2
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w = math.exp(-d2 / (2 * s * s))
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if w > 0.25:
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r = r * (1 - w) + col[0] * w
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g = g * (1 - w) + col[1] * w
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b = b * (1 - w) + col[2] * w
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return r, g, b
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write_ppm(os.path.join(out_dir, 'radar_512x384.ppm'), 512, 384, radar)
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# 2. small gradient + circle, exercises nothing fancy (256x256)
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def grad(x, y):
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inside = (x - 128) ** 2 + (y - 96) ** 2 < 48 ** 2
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return (255, 64, 32) if inside else (x % 256, y % 256, (x + y) % 256)
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write_ppm(os.path.join(out_dir, 'grad_256x256.ppm'), 256, 256, grad)
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# 3. non-32-aligned size to exercise smart-resize (500x375)
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write_ppm(os.path.join(out_dir, 'radar_500x375.ppm'), 500, 375,
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lambda x, y: radar(x * 512 // 500, y * 384 // 375))
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