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--- |
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license: mit |
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--- |
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Finetune based on ChemLLM-20B and InterViT-6B |
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## Model Usage |
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We provide an example code to run ChemVLM-26B using `transformers`. |
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You can also use our [online demo](https://v.chemllm.org/) for a quick experience of this model. |
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> Please use transformers==4.37.2 to ensure the model works normally. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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import torchvision.transforms as T |
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from PIL import Image |
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from torchvision.transforms.functional import InterpolationMode |
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IMAGENET_MEAN = (0.485, 0.456, 0.406) |
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IMAGENET_STD = (0.229, 0.224, 0.225) |
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def build_transform(input_size): |
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD |
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transform = T.Compose([ |
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), |
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), |
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T.ToTensor(), |
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T.Normalize(mean=MEAN, std=STD) |
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]) |
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return transform |
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): |
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best_ratio_diff = float('inf') |
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best_ratio = (1, 1) |
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area = width * height |
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for ratio in target_ratios: |
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target_aspect_ratio = ratio[0] / ratio[1] |
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ratio_diff = abs(aspect_ratio - target_aspect_ratio) |
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if ratio_diff < best_ratio_diff: |
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best_ratio_diff = ratio_diff |
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best_ratio = ratio |
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elif ratio_diff == best_ratio_diff: |
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: |
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best_ratio = ratio |
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return best_ratio |
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def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False): |
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orig_width, orig_height = image.size |
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aspect_ratio = orig_width / orig_height |
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# calculate the existing image aspect ratio |
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target_ratios = set( |
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(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 |
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i * j <= max_num and i * j >= min_num) |
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) |
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# find the closest aspect ratio to the target |
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target_aspect_ratio = find_closest_aspect_ratio( |
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aspect_ratio, target_ratios, orig_width, orig_height, image_size) |
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# calculate the target width and height |
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target_width = image_size * target_aspect_ratio[0] |
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target_height = image_size * target_aspect_ratio[1] |
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1] |
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# resize the image |
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resized_img = image.resize((target_width, target_height)) |
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processed_images = [] |
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for i in range(blocks): |
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box = ( |
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(i % (target_width // image_size)) * image_size, |
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(i // (target_width // image_size)) * image_size, |
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((i % (target_width // image_size)) + 1) * image_size, |
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((i // (target_width // image_size)) + 1) * image_size |
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) |
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# split the image |
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split_img = resized_img.crop(box) |
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processed_images.append(split_img) |
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assert len(processed_images) == blocks |
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if use_thumbnail and len(processed_images) != 1: |
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thumbnail_img = image.resize((image_size, image_size)) |
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processed_images.append(thumbnail_img) |
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return processed_images |
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def load_image(image_file, input_size=448, max_num=6): |
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image = Image.open(image_file).convert('RGB') |
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transform = build_transform(input_size=input_size) |
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images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) |
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pixel_values = [transform(image) for image in images] |
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pixel_values = torch.stack(pixel_values) |
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return pixel_values |
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path = "OpenGVLab/InternVL-Chat-V1-5" |
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# If you have an 80G A100 GPU, you can put the entire model on a single GPU. |
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model = AutoModel.from_pretrained( |
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path, |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage=True, |
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trust_remote_code=True).eval().cuda() |
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# Otherwise, you need to set device_map='auto' to use multiple GPUs for inference. |
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# import os |
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# os.environ["CUDA_LAUNCH_BLOCKING"] = "1" |
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# model = AutoModel.from_pretrained( |
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# path, |
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# torch_dtype=torch.bfloat16, |
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# low_cpu_mem_usage=True, |
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# trust_remote_code=True, |
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# device_map='auto').eval() |
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
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# set the max number of tiles in `max_num` |
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pixel_values = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() |
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generation_config = dict( |
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num_beams=1, |
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max_new_tokens=512, |
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do_sample=False, |
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) |
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# single-round single-image conversation |
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question = "请详细描述图片" # Please describe the picture in detail |
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response = model.chat(tokenizer, pixel_values, question, generation_config) |
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print(question, response) |
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# multi-round single-image conversation |
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question = "请详细描述图片" # Please describe the picture in detail |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) |
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print(question, response) |
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question = "请根据图片写一首诗" # Please write a poem according to the picture |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) |
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print(question, response) |
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# multi-round multi-image conversation |
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pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() |
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pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() |
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
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question = "详细描述这两张图片" # Describe the two pictures in detail |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) |
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print(question, response) |
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question = "这两张图片的相同点和区别分别是什么" # What are the similarities and differences between these two pictures |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) |
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print(question, response) |
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# batch inference (single image per sample) |
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pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() |
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pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() |
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image_counts = [pixel_values1.size(0), pixel_values2.size(0)] |
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
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questions = ["Describe the image in detail."] * len(image_counts) |
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responses = model.batch_chat(tokenizer, pixel_values, |
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image_counts=image_counts, |
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questions=questions, |
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generation_config=generation_config) |
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for question, response in zip(questions, responses): |
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print(question) |
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print(response) |
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``` |
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## License |
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This project is released under the MIT license. |
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## Acknowledgement |
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ChemVLM is built on [InternVL](https://github.com/OpenGVLab/InternVL). |
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InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work! |
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