File size: 7,032 Bytes
4830517 d6d35ed 4830517 d6d35ed 4830517 d6d35ed 4830517 d6d35ed 4830517 d6d35ed 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 4830517 8e8fbc1 d6d35ed 4830517 d6d35ed 4830517 d6d35ed 4830517 d6d35ed 4830517 d6d35ed 4830517 d6d35ed 4830517 304a0a4 d6d35ed 304a0a4 d6d35ed 4830517 304a0a4 d6d35ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
import math
from typing import List, Optional, Union
import torch
from modeling_llava import LlavaForCausalLM
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: LlavaForCausalLM = 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))
|