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"""
Oculus Processor
Handles image and text preprocessing for the Oculus model.
"""
from typing import Optional, Union, List, Dict, Any
from PIL import Image
import numpy as np
from transformers import ProcessorMixin, BatchFeature
from transformers.image_utils import ImageInput
class OculusProcessor(ProcessorMixin):
"""
Processor for Oculus model.
Combines image processing and text tokenization.
Usage:
```python
processor = OculusProcessor.from_pretrained("OceanirAI/oculus-0.2")
# Process inputs
inputs = processor(
images=image,
text="What is in this image?",
mode="text",
return_tensors="pt"
)
```
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor=None,
tokenizer=None,
**kwargs
):
super().__init__(image_processor, tokenizer)
self.image_processor = image_processor
self.tokenizer = tokenizer
# Special tokens
self.thinking_token = kwargs.get("thinking_token", "<think>")
self.thinking_end_token = kwargs.get("thinking_end_token", "</think>")
self.focus_token = kwargs.get("focus_token", "<focus>")
self.focus_end_token = kwargs.get("focus_end_token", "</focus>")
# Output mode tokens
self.mode_tokens = {
"text": "<text>",
"point": "<point>",
"box": "<box>",
"polygon": "<polygon>",
}
def __call__(
self,
images: ImageInput = None,
text: Union[str, List[str]] = None,
mode: str = "text",
think: bool = False,
return_tensors: Optional[str] = None,
**kwargs
) -> BatchFeature:
"""
Process images and text for Oculus model.
Args:
images: Input image(s)
text: Input text prompt(s)
mode: Output mode ("text", "point", "box", "polygon")
think: Enable reasoning mode
return_tensors: Tensor format ("pt", "np", etc.)
Returns:
BatchFeature with processed inputs
"""
# Process images
if images is not None:
if self.image_processor is not None:
image_features = self.image_processor(images, return_tensors=return_tensors)
else:
# Basic processing
if isinstance(images, Image.Image):
images = [images]
image_features = {"pixel_values": images}
else:
image_features = {}
# Process text
if text is not None:
# Add mode and thinking tokens
processed_text = self._format_prompt(text, mode, think)
if self.tokenizer is not None:
text_features = self.tokenizer(
processed_text,
return_tensors=return_tensors,
padding=True,
truncation=True,
**kwargs
)
else:
text_features = {"text": processed_text}
else:
text_features = {}
# Combine features
return BatchFeature(
data={
**image_features,
**text_features,
"mode": mode,
"think": think,
},
tensor_type=return_tensors
)
def _format_prompt(
self,
text: Union[str, List[str]],
mode: str,
think: bool
) -> Union[str, List[str]]:
"""Format prompt with special tokens."""
def format_single(t: str) -> str:
parts = []
# Add mode token
if mode in self.mode_tokens:
parts.append(self.mode_tokens[mode])
# Add thinking token if enabled
if think:
parts.append(self.thinking_token)
# Add prompt
parts.append(t)
return " ".join(parts)
if isinstance(text, str):
return format_single(text)
else:
return [format_single(t) for t in text]
def decode(
self,
token_ids,
skip_special_tokens: bool = True,
**kwargs
) -> str:
"""Decode token IDs to text."""
if self.tokenizer is not None:
text = self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens, **kwargs)
else:
text = str(token_ids)
# Parse thinking trace if present
thinking_trace = None
if self.thinking_token in text and self.thinking_end_token in text:
start = text.find(self.thinking_token) + len(self.thinking_token)
end = text.find(self.thinking_end_token)
thinking_trace = text[start:end].strip()
text = text[end + len(self.thinking_end_token):].strip()
return text, thinking_trace
def batch_decode(
self,
token_ids,
skip_special_tokens: bool = True,
**kwargs
) -> List[str]:
"""Decode batch of token IDs."""
return [
self.decode(ids, skip_special_tokens=skip_special_tokens, **kwargs)
for ids in token_ids
]
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
"""Load processor from pretrained."""
try:
from transformers import AutoImageProcessor, AutoTokenizer
image_processor = AutoImageProcessor.from_pretrained(
pretrained_model_name_or_path, **kwargs
)
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs
)
return cls(image_processor=image_processor, tokenizer=tokenizer, **kwargs)
except:
# Return basic processor without HF components
return cls(**kwargs)
def save_pretrained(self, save_directory: str, **kwargs):
"""Save processor to directory."""
if self.image_processor is not None:
self.image_processor.save_pretrained(save_directory)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(save_directory)
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