mirror of
https://github.com/osmarks/meme-search-engine.git
synced 2024-11-13 23:34:49 +00:00
86 lines
2.3 KiB
Python
86 lines
2.3 KiB
Python
import torch.nn
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import torch.nn.functional as F
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import torch
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import sqlite3
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import random
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import numpy
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import json
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import time
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from tqdm import tqdm
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import torch
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from model import Config, BradleyTerry
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import shared
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batch_size = 128
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device = "cuda"
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config = Config(
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d_emb=1152,
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n_hidden=1,
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n_ensemble=16,
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device=device,
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dtype=torch.float32,
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dropout=0.1
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)
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model = BradleyTerry(config).float()
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modelc, _ = shared.checkpoint_for(1500)
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model.load_state_dict(torch.load(modelc))
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params = sum(p.numel() for p in model.parameters())
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print(f"{params/1e6:.1f}M parameters")
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print(model)
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files = shared.fetch_all_files()
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ratings = {}
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model.eval()
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with torch.inference_mode():
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for bstart in tqdm(range(0, len(files), batch_size)):
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batch = files[bstart:bstart + batch_size]
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filenames = [ filename for filename, embedding in batch ]
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embs = torch.stack([ torch.Tensor(embedding) for filename, embedding in batch ])
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inputs = embs.unsqueeze(0).expand((config.n_ensemble, len(batch), config.d_emb)).to(device)
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scores = model.ensemble(inputs).float()
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mscores = torch.median(scores, dim=0).values
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for filename, mscore in zip(filenames, mscores):
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ratings[filename] = float(mscore)
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ratings = sorted(ratings.items(), key=lambda x: x[1])
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def percentile(p, n):
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base = round(p * len(ratings))
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return ratings[base:base + n]
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N = 25
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def render_memeset(p):
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filenames = percentile(p, N)
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return f"""
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<div>
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<details><summary>Reveal Memeset</summary>{p}</details>
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{''.join(f'<div><img src="{"images/" + f}" width="30%"><br><input type=checkbox id="{"col-" + str(p) + "-" + str(i)}"></div>' for i, (f, s) in enumerate(filenames))}
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</div>
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"""
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buf = """<!DOCTYPE html>"""
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probs = [0.01, 0.02, 0.05, 0.1, 0.25, 0.5, 0.75, 0.95, 0.98, 0.99]
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random.shuffle(probs)
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for p in probs:
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#for p in [0.3]:
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buf += render_memeset(p)
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buf += """
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<script>
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const computeCounts = () => {
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const counts = {}
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for (const x of document.querySelectorAll("input[type=checkbox]")) {
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const [_, percentile, index] = x.getAttribute("id").split("-")
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counts[percentile] ??= 0
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if (x.checked) counts[percentile] += 1
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}
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console.log(counts)
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}
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</script>
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"""
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with open("eval.html", "w") as f:
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f.write(buf) |