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from transformers.processing_utils import ProcessorMixin
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode, pad
from transformers.feature_extraction_sequence_utils import BatchFeature
class SimpleStarVectorProcessor(ProcessorMixin):
attributes = ["tokenizer"] # Only include tokenizer in attributes
valid_kwargs = ["size", "mean", "std"] # Add other parameters as valid kwargs
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self,
tokenizer=None, # Make tokenizer the first argument
size=224,
mean=None,
std=None,
**kwargs,
):
if mean is None:
mean = (0.48145466, 0.4578275, 0.40821073)
if std is None:
std = (0.26862954, 0.26130258, 0.27577711)
# Store these as instance variables
self.mean = mean
self.std = std
self.size = size
self.normalize = transforms.Normalize(mean=mean, std=std)
self.transform = transforms.Compose([
transforms.Lambda(lambda img: img.convert("RGB") if img.mode == "RGBA" else img),
transforms.Lambda(lambda img: self._pad_to_square(img)),
transforms.Resize(size, interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
self.normalize
])
# Initialize parent class with tokenizer
super().__init__(tokenizer=tokenizer)
def __call__(self, images=None, text=None, **kwargs) -> BatchFeature:
"""
Process images and/or text inputs.
Args:
images: Optional image input(s)
text: Optional text input(s)
**kwargs: Additional arguments
"""
if images is None and text is None:
raise ValueError("You have to specify at least one of `images` or `text`.")
image_inputs = {}
if images is not None:
if isinstance(images, (list, tuple)):
images_ = [self.transform(img) for img in images]
else:
images_ = self.transform(images)
image_inputs = {"pixel_values": images_}
text_inputs = {}
if text is not None:
text_inputs = self.tokenizer(text, **kwargs)
return BatchFeature(data={**text_inputs, **image_inputs})
def _pad_to_square(self, img):
# Calculate padding to make the image square
width, height = img.size
max_dim = max(width, height)
padding = [(max_dim - width) // 2, (max_dim - height) // 2]
padding += [max_dim - width - padding[0], max_dim - height - padding[1]]
return pad(img, padding, fill=255) # Assuming white padding
AutoProcessor.register(SimpleStarVectorProcessor, SimpleStarVectorProcessor)
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