mirror of
https://github.com/osmarks/meme-search-engine.git
synced 2024-11-14 15:54:48 +00:00
69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
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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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random.shuffle(ratings)
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N = 150
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buf = f"""<!DOCTYPE html>
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<div>
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{''.join(f'<div><img src="{"images/" + f}" width="30%"><br><input type=checkbox data-score="{s}"></div>' for i, (f, s) in enumerate(ratings[:N]))}
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</div>
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<script>
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const dump = () => {{
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const data = []
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for (const x of document.querySelectorAll("input[type=checkbox]")) {{
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data.push([parseFloat(x.getAttribute("data-score")), x.checked])
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}}
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console.log(JSON.stringify(data))
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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)
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