| 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): |
|
|
| |
| 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") |
| |
| 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, |
| ) |
|
|
| |
| 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 |
|
|
| |
| 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]) |
|
|
| |
| target_aspect_ratio = self.find_closest_aspect_ratio( |
| aspect_ratio, target_ratios, orig_width, orig_height, image_size) |
|
|
| |
| 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] |
|
|
| |
| 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_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 |
