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import math
from typing import List, Optional, Union
import torch
from PIL import Image
from transformers import ImageProcessingMixin, ProcessorMixin, SiglipImageProcessor, AutoTokenizer, AutoImageProcessor
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.tokenization_utils_base import (
PaddingStrategy,
PreTokenizedInput,
TextInput,
TruncationStrategy,
)
from transformers.utils import TensorType
class MultiCropImageProcessor(ImageProcessingMixin):
def __init__(self, model_name, max_crops=0, **kwargs):
self.processor = SiglipImageProcessor.from_pretrained(model_name)
self.crop_size = 384
self.max_crops = max_crops
self.stride_ratio = 2
def __call__(
self,
images: List[Image.Image],
max_crops: int = -1,
):
res = {
"pixel_values": [],
"coords": [],
}
if max_crops < 0:
max_crops = self.max_crops
for image in images:
outputs, output_coords = self.process_image(image, max_crops)
res["pixel_values"].append(outputs)
res["coords"].append(output_coords)
return res
def process_image(
self,
image: Image.Image,
max_crops: int
):
outputs = []
output_coords = []
outputs.append(self.processor(image, return_tensors="pt").pixel_values)
output_coords.append(torch.tensor([0.5, 0.5]))
width, height = image.size
crop_size = self.crop_size
stride = crop_size // self.stride_ratio
if (
max_crops == 0
or width <= (crop_size + stride)
and height <= (crop_size + stride)
):
outputs = torch.cat(outputs, dim=0)
output_coords = torch.cat(output_coords, dim=0)
return outputs, output_coords
total_tokens = math.inf
while total_tokens > max_crops:
total_tokens = (
math.floor((width - crop_size) / stride) + 1
) * (
math.floor((height - crop_size) / stride) + 1
)
if total_tokens > max_crops:
crop_size += 10
stride = crop_size // self.stride_ratio
stride = crop_size // self.stride_ratio
x_steps = int(math.floor((width - crop_size) / stride) + 1)
if x_steps < 1:
x_steps = 1
y_steps = int(math.floor((height - crop_size) / stride) + 1)
if y_steps < 1:
y_steps = 1
if x_steps == 1 and y_steps == 1:
outputs = torch.cat(outputs, dim=0)
output_coords = torch.cat(output_coords, dim=0)
return outputs, output_coords
x_coords = []
y_coords = []
for i in range(x_steps):
x_coords.append([i * stride, i * stride + crop_size])
if x_coords[-1][1] != width:
x_coords[-1][1] = width
for i in range(y_steps):
y_coords.append([i * stride, i * stride + crop_size])
if y_coords[-1][1] != height:
y_coords[-1][1] = height
image_parts = []
part_coords = []
for i in range(len(x_coords)):
for j in range(len(y_coords)):
image_parts.append(
image.crop(
(x_coords[i][0], y_coords[j][0], x_coords[i][1], y_coords[j][1])
)
)
part_coords.append(
torch.tensor(
[
(x_coords[i][0] + x_coords[i][1]) / 2 / width,
(y_coords[j][0] + y_coords[j][1]) / 2 / height,
]
)
)
for image_part in image_parts:
outputs.append(self.processor(image_part, return_tensors="pt").pixel_values)
for part_coord in part_coords:
output_coords.append(part_coord)
outputs = torch.cat(outputs, dim=0)
output_coords = torch.stack(output_coords, dim=0)
return outputs, output_coords
class LlavaProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
image_processor_class = MultiCropImageProcessor
tokenizer_class = "SiglipTokenizer"
def __init__(self, image_processor: MultiCropImageProcessor, tokenizer):
self.image_processor = image_processor
self.tokenizer = tokenizer
self.search_model = None
@classmethod
def from_pretrained(cls, path, trust_remote_code=True, **kwargs):
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=trust_remote_code)
image_processor = MultiCropImageProcessor(path, trust_remote_code=trust_remote_code)
return LlavaProcessor(image_processor, tokenizer)
def __call__(
self,
text: Union[
TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
] = None,
images: ImageInput = None,
model = None,
max_crops: int = 0,
num_tokens = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length=None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchFeature:
if images is not None:
processor_outputs = self.image_processor(images, max_crops)
pixel_values = processor_outputs["pixel_values"]
pixel_values = [
value.to(model.device).to(model.dtype) for value in pixel_values
]
coords = processor_outputs["coords"]
coords = [value.to(model.device).to(model.dtype) for value in coords]
image_outputs = model.vision_model(pixel_values, coords, num_tokens)
image_features = model.multi_modal_projector(image_outputs)
else:
image_features = None
text_inputs = self.tokenizer(
text,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
max_length=max_length,
)
text_inputs['input_ids'] = text_inputs['input_ids'].to(model.device)
text_inputs['attention_mask'] = text_inputs['attention_mask'].to(model.device)
return BatchFeature(data={**text_inputs, "image_features": image_features})
def batch_decode(self, *args, **kwargs):
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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