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from transformers import AutoTokenizer, AutoModel
import torch
import torchvision.transforms as T
from PIL import Image
from io import BytesIO
import requests
from torchvision.transforms.functional import InterpolationMode
class EndpointHandler():
def __init__(self, path):
# If you have an 80G A100 GPU, you can put the entire model on a single GPU.
self.model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True).eval().cuda()
self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
self.IMAGENET_MEAN = (0.485, 0.456, 0.406)
self.IMAGENET_STD = (0.229, 0.224, 0.225)
def __call__(self, data):
inputs = data.pop("inputs", data)
image_url = inputs.get("image_url")
prompt = inputs.get("prompt")
# set the max number of tiles in `max_num`
pixel_values = self.load_image(image_url, max_num=6).to(torch.bfloat16).cuda()
generation_config = dict(
num_beams=1,
max_new_tokens=1000,
do_sample=False,
)
# single-round single-image conversation
response = self.model.chat(self.tokenizer, pixel_values, prompt, generation_config)
return {"response": response}
def load_image(self, image_file, input_size=448, max_num=6):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
transform = self.build_transform(input_size=input_size)
images = self.dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
def build_transform(self, input_size):
MEAN, STD = self.IMAGENET_MEAN, self.IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def dynamic_preprocess(self, image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = self.find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio |