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from collections import defaultdict
from dataclasses import dataclass
import math
from pathlib import Path
from typing import Any
from PIL import Image
import torch
from transformers.cache_utils import DynamicCache
from selectground import SelectGround, _map_crop, _prediction
GRID_CENTERS = ((0.3, 0.3), (0.7, 0.3), (0.3, 0.7), (0.7, 0.7))
@dataclass
class _Prefix:
cache: DynamicCache
logits: torch.Tensor
position_ids: torch.Tensor
attention_mask: torch.Tensor
length: int
class SelfContrastGrounder:
"""Training-free self-contrastive grounding with six visual prefills."""
def __init__(self, checkpoint: str = "ruotian/SelectGround-8B") -> None:
self.grounder = SelectGround(checkpoint)
def predict(
self,
image: str | Path | Image.Image,
instruction: str,
*,
variant: str = "full",
) -> dict[str, Any]:
variants = {
"full": (GRID_CENTERS, True, True, 1.0),
"no_latent_distractors": ((), True, True, 1.0),
"one_latent_distractor": (GRID_CENTERS[:1], True, True, 1.0),
"no_recurrent_anchor": (GRID_CENTERS, False, True, 1.0),
"no_cross_view_evidence": (GRID_CENTERS, True, False, 1.0),
"no_anchor_proximity": (GRID_CENTERS, True, True, 0.0),
}
if variant not in variants:
raise ValueError(f"unknown self-contrast variant: {variant}")
grid_centers, use_anchor, use_evidence, proximity_weight = variants[variant]
source = (
Image.open(image).convert("RGB")
if not isinstance(image, Image.Image)
else image.convert("RGB")
)
full_box = (0, 0, source.width, source.height)
views: dict[str, tuple[tuple[int, int, int, int], Image.Image]] = {
"full": (full_box, source)
}
candidates = []
prefixes = {}
p0, prefixes["full"] = self._observe(source, instruction)
candidates.append(self._candidate("p0", p0, full_box, source.size))
if use_anchor and p0["point"] is not None:
box = _crop_box(tuple(p0["point"]), source.size, 0.40)
views["q0"] = (box, _view(source, box))
for index, center in enumerate(grid_centers):
point = (center[0] * source.width, center[1] * source.height)
box = _crop_box(point, source.size, 0.60)
views[f"grid_{index}"] = (box, _view(source, box))
for name, (box, view) in tuple(views.items())[1:]:
prediction, prefixes[name] = self._observe(view, instruction)
candidates.append(self._candidate(name, prediction, box, source.size))
evidence = {}
for view_name, (box, _) in views.items():
visible = {
candidate["name"]: response
for candidate in candidates
if candidate["point"] is not None
and (response := _response(candidate["point"], box)) is not None
}
scores = self._score(prefixes.pop(view_name), list(visible.values()))
evidence[view_name] = {
name: {"response": response, **scores[response]}
for name, response in visible.items()
}
selected = _select(
candidates,
evidence,
source.size,
proximity_weight=proximity_weight,
use_evidence=use_evidence,
)
point = selected["point"]
normalized = (
[1000 * point[0] / source.width, 1000 * point[1] / source.height]
if point is not None
else None
)
return {
"method": "SelectGround+SelfContrast",
"variant": variant,
"point": point,
"normalized_point": normalized,
"raw_response": selected["raw_response"],
"selected_candidate": selected["name"],
}
def _candidate(
self,
name: str,
prediction: dict[str, Any],
box: tuple[int, int, int, int],
source_size: tuple[int, int],
) -> dict[str, Any]:
mapped = (
prediction
if name == "p0" or prediction["point"] is None
else _map_crop(prediction, box, source_size, 2.0)
)
return {
"name": name,
"point": mapped["point"],
"source_view": "full" if name == "p0" else name,
"raw_response": prediction["raw_response"],
}
@torch.inference_mode()
def _observe(
self, image: Image.Image, instruction: str
) -> tuple[dict[str, Any], _Prefix]:
inputs = self.grounder._inputs(image, instruction, False)
input_ids = inputs["input_ids"]
length = int(input_ids.shape[1])
position_ids, _ = self.grounder.core.get_rope_index(
input_ids,
inputs.get("image_grid_thw"),
inputs.get("video_grid_thw"),
attention_mask=inputs.get("attention_mask"),
)
cache = DynamicCache(config=self.grounder.core.language_model.config)
output = self.grounder.model(
**inputs,
past_key_values=cache,
position_ids=position_ids,
cache_position=torch.arange(length, device=self.grounder.device),
use_cache=True,
logits_to_keep=1,
)
logits = output.logits[:, -1, :].detach()
raw = self.grounder._decode(
logits, cache, position_ids[:, :, -1:] + 1, None
)
cache.crop(length)
if cache.get_seq_length() != length:
raise RuntimeError("could not restore the visual prefix after decoding")
return (
_prediction(raw, image.size, integer=False),
_Prefix(cache, logits, position_ids, inputs["attention_mask"], length),
)
@torch.inference_mode()
def _score(
self, prefix: _Prefix, responses: list[str]
) -> dict[str, dict[str, float | int]]:
unique = list(dict.fromkeys(responses))
if not unique:
return {}
encoded = [_token_ids(self.grounder, response) for response in unique]
first = torch.log_softmax(prefix.logits.float(), -1)
logps = [[float(first[0, values[0]])] for values in encoded]
maximum = max(map(len, encoded))
if maximum > 1:
tokenizer = self.grounder.processor.tokenizer
pad = tokenizer.pad_token_id or tokenizer.eos_token_id
continuation = torch.full(
(len(encoded), maximum - 1),
int(pad),
dtype=torch.long,
device=self.grounder.device,
)
mask = torch.zeros_like(continuation, dtype=torch.bool)
for index, values in enumerate(encoded):
if len(values) > 1:
continuation[index, : len(values) - 1] = torch.tensor(
values[:-1], device=self.grounder.device
)
mask[index, : len(values) - 1] = True
prefix.cache.batch_repeat_interleave(len(encoded))
positions = prefix.position_ids.repeat_interleave(len(encoded), dim=-2)
offsets = torch.arange(maximum - 1, device=self.grounder.device).view(
*([1] * (positions.ndim - 1)), -1
)
output = self.grounder.model(
input_ids=continuation,
past_key_values=prefix.cache,
attention_mask=torch.cat(
(prefix.attention_mask.repeat(len(encoded), 1), mask.long()), 1
),
position_ids=positions[..., -1:] + 1 + offsets,
cache_position=torch.arange(
prefix.length,
prefix.length + maximum - 1,
device=self.grounder.device,
),
use_cache=True,
)
for index, values in enumerate(encoded):
if len(values) <= 1:
continue
logits = output.logits[index, : len(values) - 1].float()
labels = torch.tensor(values[1:], device=self.grounder.device)
selected = torch.log_softmax(logits, -1).gather(1, labels[:, None])[:, 0]
logps[index].extend(float(value) for value in selected)
return {
response: {
"token_count": len(values),
"mean_logprob": sum(values_logps) / len(values),
}
for response, values, values_logps in zip(unique, encoded, logps, strict=True)
}
def _crop_box(
point: tuple[float, float], size: tuple[int, int], fraction: float
) -> tuple[int, int, int, int]:
width, height = size
crop_width = min(width, max(320, round(fraction * width)))
crop_height = min(height, max(320, round(fraction * height)))
left = round(min(max(0.0, point[0] - crop_width / 2), width - crop_width))
top = round(min(max(0.0, point[1] - crop_height / 2), height - crop_height))
return left, top, left + crop_width, top + crop_height
def _view(source: Image.Image, box: tuple[int, int, int, int]) -> Image.Image:
crop = source.crop(box)
return crop.resize(
(2 * crop.width, 2 * crop.height), Image.Resampling.LANCZOS
)
def _response(point: list[float], box: tuple[int, int, int, int]) -> str | None:
left, top, right, bottom = box
x, y = map(float, point)
if not left <= x < right or not top <= y < bottom:
return None
return (
f"[{round(1000 * (x - left) / (right - left))},"
f"{round(1000 * (y - top) / (bottom - top))}]"
)
def _token_ids(grounder: SelectGround, response: str) -> list[int]:
values = grounder.processor.tokenizer(
response, add_special_tokens=False
)["input_ids"]
if values and isinstance(values[0], list):
values = values[0]
result = [int(value) for value in values]
if not result:
raise ValueError(f"empty tokenization for {response!r}")
return result
def _zscore(values: list[float]) -> list[float]:
mean = sum(values) / len(values)
std = math.sqrt(sum((value - mean) ** 2 for value in values) / len(values))
return [(value - mean) / max(std, 1e-6) for value in values]
def _select(
candidates: list[dict[str, Any]],
evidence: dict[str, dict[str, dict[str, Any]]],
size: tuple[int, int],
*,
proximity_weight: float,
use_evidence: bool,
) -> dict[str, Any]:
eligible = [candidate for candidate in candidates if candidate["point"] is not None]
if not eligible:
return candidates[0]
by_name = {candidate["name"]: candidate for candidate in eligible}
accumulated = defaultdict(list)
for view_name, values in evidence.items():
unique = {}
for name, value in values.items():
if name in by_name:
unique.setdefault(value["response"], float(value["mean_logprob"]))
if not unique:
continue
normalized = dict(zip(unique, _zscore(list(unique.values())), strict=True))
for name, value in values.items():
if name in by_name and by_name[name]["source_view"] != view_name:
accumulated[name].append(normalized[value["response"]])
if use_evidence:
eligible = [candidate for candidate in eligible if accumulated[candidate["name"]]]
if not eligible:
return candidates[0]
likelihood = _zscore(
[
sum(accumulated[candidate["name"]])
/ len(accumulated[candidate["name"]])
for candidate in eligible
]
)
else:
likelihood = [0.0] * len(eligible)
by_name = {candidate["name"]: candidate for candidate in eligible}
anchor = by_name.get("q0", by_name.get("p0", eligible[0]))["point"]
width, height = size
proximity = _zscore(
[
-math.hypot(
(candidate["point"][0] - anchor[0]) / width,
(candidate["point"][1] - anchor[1]) / height,
)
for candidate in eligible
]
)
scores = [
likelihood[index] + proximity_weight * proximity[index]
for index in range(len(eligible))
]
return eligible[max(range(len(eligible)), key=lambda index: (scores[index], -index))]
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