mirror of
https://github.com/osmarks/meme-search-engine.git
synced 2024-11-13 07:19:54 +00:00
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
|
import torch.nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
import torch
|
||
|
|
import sqlite3
|
||
|
|
import random
|
||
|
|
import numpy
|
||
|
|
import json
|
||
|
|
import time
|
||
|
|
from tqdm import tqdm
|
||
|
|
|
||
|
|
from model import Config, BradleyTerry
|
||
|
|
import shared
|
||
|
|
|
||
|
|
batch_size = 128
|
||
|
|
num_pairs = batch_size * 1024
|
||
|
|
device = "cuda"
|
||
|
|
|
||
|
|
config = Config(
|
||
|
|
d_emb=1152,
|
||
|
|
n_hidden=1,
|
||
|
|
n_ensemble=16,
|
||
|
|
device=device,
|
||
|
|
dtype=torch.bfloat16,
|
||
|
|
dropout=0.5
|
||
|
|
)
|
||
|
|
model = BradleyTerry(config)
|
||
|
|
modelc, _ = shared.checkpoint_for(2250)
|
||
|
|
model.load_state_dict(torch.load(modelc))
|
||
|
|
params = sum(p.numel() for p in model.parameters())
|
||
|
|
print(f"{params/1e6:.1f}M parameters")
|
||
|
|
print(model)
|
||
|
|
|
||
|
|
files = shared.fetch_all_files()
|
||
|
|
variance = {}
|
||
|
|
|
||
|
|
pairs = []
|
||
|
|
for _ in range(num_pairs):
|
||
|
|
pairs.append(tuple(random.sample(files, 2)))
|
||
|
|
|
||
|
|
model.eval()
|
||
|
|
with torch.inference_mode():
|
||
|
|
for bstart in tqdm(range(0, len(pairs), batch_size)):
|
||
|
|
batch = pairs[bstart:bstart + batch_size]
|
||
|
|
filenames = [ (f1, f2) for ((f1, e1), (f2, e2)) in batch ]
|
||
|
|
embs = torch.stack([ torch.stack((torch.Tensor(e1).to(config.dtype), torch.Tensor(e2).to(config.dtype))) for ((f1, e1), (f2, e2)) in batch ])
|
||
|
|
inputs = embs.unsqueeze(0).expand((config.n_ensemble, batch_size, 2, config.d_emb)).to(device)
|
||
|
|
win_probs = model(inputs)
|
||
|
|
#print(win_probs.shape)
|
||
|
|
batchvar = torch.var(win_probs, dim=0)
|
||
|
|
for filename, var in zip(filenames, batchvar):
|
||
|
|
variance[filename] = float(var)
|
||
|
|
|
||
|
|
top = sorted(variance.items(), key=lambda x: -x[1])
|
||
|
|
with open("top.json", "w") as f:
|
||
|
|
json.dump(top[:256], f)
|