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from __future__ import annotations
import json
import math
import os
import random
from pathlib import Path
from typing import Any
import numpy as np
import torch
import torch.nn.functional as F
DATASET_NAME = "nlphuji/flickr30k"
DATASET_SPLIT = "test"
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def read_json(path: str | os.PathLike[str]) -> dict[str, Any]:
with open(path, encoding="utf-8") as f:
return json.load(f)
def write_json(path: str | os.PathLike[str], value: Any) -> None:
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
json.dump(value, f, indent=2, ensure_ascii=False)
def normalized(x: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
return F.normalize(x.float(), dim=-1, eps=eps)
def pairwise_cosine_distance(x: np.ndarray) -> np.ndarray:
x = x.astype(np.float64, copy=False)
x /= np.linalg.norm(x, axis=1, keepdims=True).clip(min=1e-12)
d = 1.0 - x @ x.T
np.fill_diagonal(d, 0.0)
scale = np.median(d[d > 0])
return d / max(float(scale), 1e-12)
def linear_cka(x: torch.Tensor, y: torch.Tensor) -> float:
x = x.float() - x.float().mean(0, keepdim=True)
y = y.float() - y.float().mean(0, keepdim=True)
xty = x.T @ y
numerator = (xty * xty).sum()
xx = x.T @ x
yy = y.T @ y
denominator = torch.sqrt((xx * xx).sum() * (yy * yy).sum())
return float((numerator / denominator.clamp_min(1e-12)).item())
def retrieval_metrics(
image_features: torch.Tensor,
text_features: torch.Tensor,
ks: tuple[int, ...] = (1, 5, 10),
) -> dict[str, float]:
image_features = normalized(image_features)
text_features = normalized(text_features)
similarities = image_features @ text_features.T
n = similarities.shape[0]
truth = torch.arange(n, device=similarities.device)
i2t_order = similarities.argsort(dim=1, descending=True)
t2i_order = similarities.T.argsort(dim=1, descending=True)
i2t_rank = (i2t_order == truth[:, None]).nonzero()[:, 1]
t2i_rank = (t2i_order == truth[:, None]).nonzero()[:, 1]
result: dict[str, float] = {}
for k in ks:
result[f"i2t_r@{k}"] = float((i2t_rank < k).float().mean().item())
result[f"t2i_r@{k}"] = float((t2i_rank < k).float().mean().item())
result["i2t_median_rank"] = float(i2t_rank.float().median().item() + 1)
result["t2i_median_rank"] = float(t2i_rank.float().median().item() + 1)
result["chance_r@1"] = 1.0 / max(n, 1)
return result
def batch_indices(n: int, batch_size: int, shuffle: bool = False, seed: int = 0):
order = np.arange(n)
if shuffle:
rng = np.random.default_rng(seed)
rng.shuffle(order)
for start in range(0, n, batch_size):
yield order[start : start + batch_size]
def sliced_wasserstein(
x: torch.Tensor,
y: torch.Tensor,
num_projections: int = 64,
) -> torch.Tensor:
"""Differentiable empirical sliced W2 for equal-sized minibatches."""
n = min(x.shape[0], y.shape[0])
x = x[:n]
y = y[:n]
directions = torch.randn(
x.shape[-1], num_projections, device=x.device, dtype=x.dtype
)
directions = F.normalize(directions, dim=0)
x_proj = (x @ directions).sort(dim=0).values
y_proj = (y @ directions).sort(dim=0).values
return (x_proj - y_proj).square().mean()
def cosine_isometry_loss(source: torch.Tensor, mapped: torch.Tensor) -> torch.Tensor:
source = normalized(source)
mapped = normalized(mapped)
source_gram = source @ source.T
mapped_gram = mapped @ mapped.T
mask = ~torch.eye(source.shape[0], dtype=torch.bool, device=source.device)
return (source_gram[mask] - mapped_gram[mask]).square().mean()
def cosine_loss(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return 1.0 - F.cosine_similarity(x.float(), y.float(), dim=-1).mean()
def dtype_for_device(device: str) -> torch.dtype:
return torch.bfloat16 if device.startswith("cuda") else torch.float32
def parameter_count(module: torch.nn.Module) -> int:
return sum(p.numel() for p in module.parameters())
def cosine_schedule(step: int, steps: int, warmup: int) -> float:
if step < warmup:
return (step + 1) / max(1, warmup)
progress = (step - warmup) / max(1, steps - warmup)
return 0.5 * (1.0 + math.cos(math.pi * progress))
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