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"""
Data processors for images and text
"""
import torch
from PIL import Image
import torchvision.transforms as transforms
from typing import List, Dict, Any, Optional
import logging
logger = logging.getLogger(__name__)
class ImageProcessor:
"""Image preprocessing for CLIP vision encoder"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.image_size = config["data"]["image_size"]
# CLIP normalization values
self.mean = config["data"]["image_mean"]
self.std = config["data"]["image_std"]
# Setup transforms
self.transform = self._setup_transforms()
def _setup_transforms(self):
"""Setup image transformations"""
transform_list = [
transforms.Resize((self.image_size, self.image_size)),
transforms.ToTensor(),
transforms.Normalize(mean=self.mean, std=self.std)
]
# Add augmentations if enabled
if self.config["data"]["augmentation"]["enabled"]:
aug_transforms = []
# Random resized crop
if self.config["data"]["augmentation"].get("random_resized_crop"):
scale = self.config["data"]["augmentation"]["random_resized_crop"]
aug_transforms.append(
transforms.RandomResizedCrop(
self.image_size,
scale=(scale, 1.0)
)
)
# Color jitter
if self.config["data"]["augmentation"].get("color_jitter"):
brightness = self.config["data"]["augmentation"]["color_jitter"]
aug_transforms.append(
transforms.ColorJitter(brightness=brightness)
)
# Horizontal flip
if self.config["data"]["augmentation"].get("horizontal_flip"):
prob = self.config["data"]["augmentation"]["horizontal_flip"]
aug_transforms.append(
transforms.RandomHorizontalFlip(p=prob)
)
# Insert augmentations before normalization
transform_list = (
transform_list[:-2] + # Resize, ToTensor
aug_transforms +
transform_list[-2:] # Normalize
)
return transforms.Compose(transform_list)
def __call__(self, image: Image.Image) -> torch.Tensor:
"""Process a single image"""
if not isinstance(image, Image.Image):
raise ValueError(f"Expected PIL Image, got {type(image)}")
return self.transform(image)
def process_batch(self, images: List[Image.Image]) -> torch.Tensor:
"""Process a batch of images"""
processed = []
for img in images:
processed.append(self(img))
return torch.stack(processed)
class TextProcessor:
"""Text preprocessing for conversations"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.max_length = config["data"]["max_length"]
# Conversation formatting
conv_config = config["data"]["conversation"]
self.system_message = conv_config.get("system_message", "")
self.user_prefix = conv_config.get("user_prefix", "Human: ")
self.assistant_prefix = conv_config.get("assistant_prefix", "Assistant: ")
self.turn_separator = conv_config.get("turn_separator", "\n")
def format_conversation(self, conversations: List[Dict[str, str]]) -> str:
"""Format conversation into training text with robust error handling"""
formatted_parts = []
# Add system message if present
if self.system_message:
formatted_parts.append(self.system_message)
# Ensure conversations is a valid list
if not isinstance(conversations, list):
conversations = []
# Process conversation turns with error handling
for turn in conversations:
try:
if not isinstance(turn, dict):
continue
role = turn.get("from", "").lower().strip()
content = turn.get("value", "")
# Clean and validate content
if not isinstance(content, str):
content = str(content) if content else ""
content = content.strip()
if not content:
continue
# Remove problematic characters that might cause issues
content = content.replace('\x00', '').replace('\n\n\n', '\n\n')
if role in ["human", "user"]:
formatted_parts.append(f"{self.user_prefix}{content}")
elif role in ["gpt", "assistant", "ai"]:
formatted_parts.append(f"{self.assistant_prefix}{content}")
else:
# Default to human if role is unclear
formatted_parts.append(f"{self.user_prefix}{content}")
except Exception as e:
logger.debug(f"Error processing conversation turn: {e}")
continue
# Ensure we have at least some content
if not formatted_parts:
return f"{self.user_prefix}What do you see in this image?{self.turn_separator}{self.assistant_prefix}I can see an image."
return self.turn_separator.join(formatted_parts)
def add_image_token(self, text: str, has_image: bool = True) -> str:
"""Add image token to text if image is present"""
if has_image:
image_token = self.config.get("special_tokens", {}).get("image_token", "<image>")
return f"{image_token}\n{text}"
return text
def validate_text(self, text: str) -> bool:
"""Validate text meets filtering criteria - more lenient validation"""
if not isinstance(text, str):
return False
# Basic cleanup
text = text.strip()
# Check for completely empty content
if not text:
return False
# More lenient length check - just ensure it's not absurdly long or short
text_length = len(text)
if text_length < 5: # Very short
return False
if text_length > 2000: # Very long
return False
# Check for basic structure (should have some content)
if len(text.split()) < 2: # Less than 2 words
return False
return True
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