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"""OpticalDNA model entry points.
This module provides public OpticalDNA class names while preserving compatibility
with the underlying OCR-style vision-language implementation.
"""
from __future__ import annotations
import torch
from .modeling_deepseekocr import (
DeepseekOCRConfig as _BaseOpticalDNAConfig,
DeepseekOCRModel as _BaseOpticalDNAModel,
DeepseekOCRForCausalLM as _BaseOpticalDNAForCausalLM,
)
from .inference import (
extract_decoder_features_from_images,
extract_visual_features_from_images,
generate_from_images,
)
from .prompts import PromptGenerator, PromptLength, TaskType
class OpticalDNAConfig(_BaseOpticalDNAConfig):
"""Configuration class for OpticalDNA."""
model_type = "opticaldna"
class OpticalDNAModel(_BaseOpticalDNAModel):
"""OpticalDNA base model."""
config_class = OpticalDNAConfig
class OpticalDNAForCausalLM(_BaseOpticalDNAForCausalLM):
"""OpticalDNA visual encoder + autoregressive genomic document decoder."""
config_class = OpticalDNAConfig
# CLIP creates this deterministic index buffer at module construction time with
# persistent=False, so it is intentionally absent from safetensors checkpoints.
_keys_to_ignore_on_load_missing = [
r"model\.vision_model\.embeddings\.position_ids",
]
_allowed_nonpersistent_missing_keys = {
"model.vision_model.embeddings.position_ids",
}
@staticmethod
def _is_nonpersistent_buffer(model, key: str) -> bool:
"""Return True only when ``key`` resolves to a non-persistent torch buffer."""
parts = key.split(".")
module = model
try:
for part in parts[:-1]:
module = getattr(module, part)
except AttributeError:
return False
name = parts[-1]
return (
name in getattr(module, "_buffers", {})
and name in getattr(module, "_non_persistent_buffers_set", set())
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
"""Load OpticalDNA weights strictly; partial/random fallback is not allowed."""
return_loading_info = bool(kwargs.pop("output_loading_info", False))
model, loading_info = super().from_pretrained(
pretrained_model_name_or_path,
*model_args,
output_loading_info=True,
**kwargs,
)
loading_info = dict(loading_info)
raw_missing = list(loading_info.get("missing_keys") or [])
remaining_missing = []
for key in raw_missing:
if key in cls._allowed_nonpersistent_missing_keys:
if not cls._is_nonpersistent_buffer(model, key):
del model
raise RuntimeError(
f"Allowed derived key {key!r} is not a non-persistent buffer; "
"refusing to relax strict checkpoint loading."
)
continue
remaining_missing.append(key)
loading_info["missing_keys"] = remaining_missing
problems = {
key: value
for key, value in loading_info.items()
if key in {"missing_keys", "unexpected_keys", "mismatched_keys", "error_msgs"}
and value
}
if problems:
del model
raise RuntimeError(
"OpticalDNA checkpoint did not load exactly; refusing to continue with "
f"partially/randomly initialized parameters. Loading info: {problems}"
)
return (model, loading_info) if return_loading_info else model
@staticmethod
def default_prompt(length: PromptLength = PromptLength.SHORT) -> str:
"""Return the default T1 Free-OCR prompt."""
return PromptGenerator().free_ocr(length=PromptLength(length))
@staticmethod
def build_prompt(
task: TaskType,
length: PromptLength = PromptLength.SHORT,
sample=None,
) -> str:
"""Build one of the released T1--T6 OpticalDNA task prompts."""
return PromptGenerator().build(
task=TaskType(task),
length=PromptLength(length),
sample=sample or {},
)
def encode_visual_features(self, images, images_spatial_crop):
"""Return projected/fused visual tokens before the language Decoder.
This method intentionally lives in ``modeling_opticaldna.py`` because it
defines OpticalDNA-specific feature semantics. It reuses the frozen model
components created by the OCR backbone but does not execute any language
Decoder layer.
"""
backbone = self.get_model()
sam_model = getattr(backbone, "sam_model", None)
vision_model = getattr(backbone, "vision_model", None)
projector = getattr(backbone, "projector", None)
page_fusion_layer = getattr(backbone, "page_fusion_layer", None)
if any(x is None for x in (sam_model, vision_model, projector, page_fusion_layer)):
raise RuntimeError("OpticalDNA visual encoder components are not available.")
if images is None or images_spatial_crop is None:
raise ValueError("images and images_spatial_crop are required.")
document_features = []
for image, crop_shape in zip(images, images_spatial_crop):
patches = image[0]
image_ori = image[1]
patches, image_ori = backbone.ensure_page_dim_patches_ori(patches, image_ori)
n_img_p, n_page_p, n_patch, c_p, h_p, w_p = patches.shape
n_img_o, n_page_o, c_o, h_o, w_o = image_ori.shape
patches_flat = patches.view(n_img_p * n_page_p * n_patch, c_p, h_p, w_p)
image_ori_flat = image_ori.view(n_img_o * n_page_o, c_o, h_o, w_o)
has_local_crops = torch.count_nonzero(patches).item() != 0
if has_local_crops:
local_sam = sam_model(patches_flat)
local_vit = vision_model(patches_flat, local_sam)
local_per_page = torch.cat(
(local_vit[:, 1:], local_sam.flatten(2).permute(0, 2, 1)),
dim=-1,
)
local_per_page = projector(local_per_page)
local_tail_shape = list(local_per_page.shape[1:])
local_permute = local_per_page.view(
*([n_img_p, n_page_p, n_patch] + local_tail_shape)
).permute(0, 2, 1, 3, 4)
local_features = page_fusion_layer(
local_permute.view(*([n_img_p * n_patch, n_page_p] + local_tail_shape))
)
else:
local_features = None
global_sam = sam_model(image_ori_flat)
global_vit = vision_model(image_ori_flat, global_sam)
global_per_page = torch.cat(
(global_vit[:, 1:], global_sam.flatten(2).permute(0, 2, 1)),
dim=-1,
)
global_per_page = projector(global_per_page)
global_tail_shape = list(global_per_page.shape[1:])
global_features = page_fusion_layer(
global_per_page.view(*([n_img_o, n_page_o] + global_tail_shape))
).view(*([n_img_o] + global_tail_shape))
# Public inference currently accepts one document per call, matching
# the existing backbone forward path where n_img_o == 1.
if n_img_o != 1:
raise RuntimeError(
"OpticalDNA visual feature extraction currently expects one document "
f"per call; got n_img={n_img_o}."
)
_, global_hw, global_dim = global_features.shape
global_h = global_w = int(global_hw ** 0.5)
if global_h * global_w != global_hw:
raise RuntimeError(
f"Global visual token count is not square: {global_hw}."
)
global_features = global_features.view(global_h, global_w, global_dim)
global_features = torch.cat(
[
global_features,
backbone.image_newline[None, None, :].expand(global_h, 1, global_dim),
],
dim=1,
).view(-1, global_dim)
if local_features is not None:
_, local_hw, local_dim = local_features.shape
local_h = local_w = int(local_hw ** 0.5)
if local_h * local_w != local_hw:
raise RuntimeError(
f"Local visual token count is not square: {local_hw}."
)
width_crop_num = int(crop_shape[0])
height_crop_num = int(crop_shape[1])
local_features = (
local_features.view(
height_crop_num,
width_crop_num,
local_h,
local_w,
local_dim,
)
.permute(0, 2, 1, 3, 4)
.reshape(
height_crop_num * local_h,
width_crop_num * local_w,
local_dim,
)
)
local_features = torch.cat(
[
local_features,
backbone.image_newline[None, None, :].expand(
height_crop_num * local_h,
1,
local_dim,
),
],
dim=1,
).view(-1, local_dim)
features = torch.cat(
[local_features, global_features, backbone.view_seperator[None, :]],
dim=0,
)
else:
features = torch.cat(
[global_features, backbone.view_seperator[None, :]],
dim=0,
)
document_features.append(features)
return document_features
def generate_document(self, tokenizer, images, prompt=None, **kwargs) -> str:
"""Generate Decoder output; default prompt is short T1 / ``Free OCR.``."""
return generate_from_images(self, tokenizer, images, prompt, **kwargs)
def extract_visual_features(self, images, **kwargs):
"""Extract OpticalDNA visual features without executing the Decoder."""
return extract_visual_features_from_images(self, images, **kwargs)
def extract_features(self, images, **kwargs):
"""Alias for visual feature extraction used by downstream tasks."""
return self.extract_visual_features(images, **kwargs)
def extract_decoder_features(self, tokenizer, images, prompt=None, **kwargs):
"""Extract prompt-conditioned Decoder hidden states."""
return extract_decoder_features_from_images(
self,
tokenizer,
images,
prompt=prompt,
**kwargs,
)
class OpticalDNAProcessor:
"""Placeholder processor name for configuration compatibility."""
pass
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