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--- |
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license: mit |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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base_model: |
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- OpenGVLab/InternViT-6B-448px-V1-5 |
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- internlm/internlm2-chat-20b |
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base_model_relation: merge |
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language: |
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- multilingual |
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tags: |
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- internvl |
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- vision |
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- ocr |
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- multi-image |
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- video |
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- custom_code |
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--- |
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# InternVL-Chat-V1-5 |
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[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) |
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[\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/) |
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## Introduction |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg" alt="Image Description" width="300" height="300"> |
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</p> |
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> _Two interns holding hands, symbolizing the integration of InternViT and InternLM._ |
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We introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. |
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We introduce three simple designs: |
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1. **Strong Vision Encoder:** we explored a continuous learning strategy for the large-scale vision foundation model---InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. |
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2. **Dynamic High-Resolution:** we divide images into tiles ranging from 1 to 40 of 448 × 448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input during inference. |
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3. **High-Quality Bilingual Dataset:** we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks. |
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## Model Details |
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- **Model Type:** multimodal large language model (MLLM) |
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- **Model Stats:** |
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- Architecture: [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) + MLP + [InternLM2-Chat-20B](https://huggingface.co/internlm/internlm2-chat-20b) |
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- Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution). |
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- Params: 25.5B |
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- **Training Strategy:** |
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- Learnable component in the pre-training stage: ViT + MLP |
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- Learnable component in the fine-tuning stage: ViT + MLP + LLM |
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- For more details on training hyperparameters, please see our [blog](https://internvl.github.io/blog/2024-04-30-InternVL-1.5/). |
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## Architecture |
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## Performance |
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- We simultaneously use [InternVL](https://github.com/OpenGVLab/InternVL) and [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit. |
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- Please note that evaluating the same model using different testing toolkits like [InternVL](https://github.com/OpenGVLab/InternVL) and [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) can result in slight differences, which is normal. Updates to code versions and variations in environment and hardware can also cause minor discrepancies in results. |
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Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information. |
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## Examples |
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## Quick Start |
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We provide an example code to run InternVL-Chat-V1-5 using `transformers`. |
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We also welcome you to experience the InternVL2 series models in our [online demo](https://internvl.opengvlab.com/). |
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> Please use transformers==4.37.2 to ensure the model works normally. |
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### Model Loading |
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#### 16-bit (bf16 / fp16) |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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path = "OpenGVLab/InternVL-Chat-V1-5" |
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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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``` |
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#### BNB 8-bit Quantization |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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path = "OpenGVLab/InternVL-Chat-V1-5" |
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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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load_in_8bit=True, |
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low_cpu_mem_usage=True, |
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trust_remote_code=True).eval() |
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``` |
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#### BNB 4-bit Quantization |
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> **⚠️ Warning:** Due to significant quantization errors with BNB 4-bit quantization on InternViT-6B, the model may produce nonsensical outputs and fail to understand images. Therefore, please avoid using BNB 4-bit quantization. |
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#### Multiple GPUs |
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The reason for writing the code this way is to avoid errors that occur during multi-GPU inference due to tensors not being on the same device. By ensuring that the first and last layers of the large language model (LLM) are on the same device, we prevent such errors. |
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```python |
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import math |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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def split_model(model_name): |
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device_map = {} |
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world_size = torch.cuda.device_count() |
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num_layers = {'Mini-InternVL-2B-V1-5': 24, 'Mini-InternVL-4B-V1-5': 32, 'InternVL-Chat-V1-5': 48}[model_name] |
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# Since the first GPU will be used for ViT, treat it as half a GPU. |
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num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5)) |
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num_layers_per_gpu = [num_layers_per_gpu] * world_size |
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num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5) |
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layer_cnt = 0 |
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for i, num_layer in enumerate(num_layers_per_gpu): |
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for j in range(num_layer): |
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device_map[f'language_model.model.layers.{layer_cnt}'] = i |
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layer_cnt += 1 |
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device_map['vision_model'] = 0 |
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device_map['mlp1'] = 0 |
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device_map['language_model.model.tok_embeddings'] = 0 |
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device_map['language_model.model.embed_tokens'] = 0 |
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device_map['language_model.output'] = 0 |
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device_map['language_model.model.norm'] = 0 |
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device_map['language_model.lm_head'] = 0 |
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device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 |
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return device_map |
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path = "OpenGVLab/InternVL-Chat-V1-5" |
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device_map = split_model('InternVL-Chat-V1-5') |
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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=device_map).eval() |
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``` |
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### Inference with Transformers |
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```python |
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import numpy as np |
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import torch |
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import torchvision.transforms as T |
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from decord import VideoReader, cpu |
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from PIL import Image |
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from torchvision.transforms.functional import InterpolationMode |
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from transformers import AutoModel, AutoTokenizer |
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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=12, 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=12): |
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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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# If you have an 80G A100 GPU, you can put the entire model on a single GPU. |
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# Otherwise, you need to load a model using multiple GPUs, please refer to the `Multiple GPUs` section. |
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path = 'OpenGVLab/InternVL-Chat-V1-5' |
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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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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) |
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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=12).to(torch.bfloat16).cuda() |
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generation_config = dict(max_new_tokens=1024, do_sample=True) |
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# pure-text conversation (纯文本对话) |
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question = 'Hello, who are you?' |
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response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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question = 'Can you tell me a story?' |
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response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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# single-image single-round conversation (单图单轮对话) |
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question = '<image>\nPlease describe the image shortly.' |
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response = model.chat(tokenizer, pixel_values, question, generation_config) |
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print(f'User: {question}\nAssistant: {response}') |
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# single-image multi-round conversation (单图多轮对话) |
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question = '<image>\nPlease describe the image 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(f'User: {question}\nAssistant: {response}') |
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question = 'Please write a poem according to the image.' |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像) |
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pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda() |
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pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda() |
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
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question = '<image>\nDescribe the two images in detail.' |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
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history=None, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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question = 'What are the similarities and differences between these two images.' |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
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history=history, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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# multi-image multi-round conversation, separate images (多图多轮对话，独立图像) |
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pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda() |
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pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda() |
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
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num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)] |
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question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.' |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
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num_patches_list=num_patches_list, |
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history=None, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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question = 'What are the similarities and differences between these two images.' |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
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num_patches_list=num_patches_list, |
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history=history, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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# batch inference, single image per sample (单图批处理) |
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pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda() |
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pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda() |
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num_patches_list = [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 = ['<image>\nDescribe the image in detail.'] * len(num_patches_list) |
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responses = model.batch_chat(tokenizer, pixel_values, |
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num_patches_list=num_patches_list, |
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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(f'User: {question}\nAssistant: {response}') |
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# video multi-round conversation (视频多轮对话) |
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def get_index(bound, fps, max_frame, first_idx=0, num_segments=32): |
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if bound: |
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start, end = bound[0], bound[1] |
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else: |
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start, end = -100000, 100000 |
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start_idx = max(first_idx, round(start * fps)) |
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end_idx = min(round(end * fps), max_frame) |
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seg_size = float(end_idx - start_idx) / num_segments |
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frame_indices = np.array([ |
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int(start_idx + (seg_size / 2) + np.round(seg_size * idx)) |
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for idx in range(num_segments) |
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]) |
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return frame_indices |
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def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32): |
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vr = VideoReader(video_path, ctx=cpu(0), num_threads=1) |
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max_frame = len(vr) - 1 |
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fps = float(vr.get_avg_fps()) |
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pixel_values_list, num_patches_list = [], [] |
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transform = build_transform(input_size=input_size) |
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frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments) |
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for frame_index in frame_indices: |
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img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB') |
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img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num) |
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pixel_values = [transform(tile) for tile in img] |
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pixel_values = torch.stack(pixel_values) |
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num_patches_list.append(pixel_values.shape[0]) |
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pixel_values_list.append(pixel_values) |
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pixel_values = torch.cat(pixel_values_list) |
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return pixel_values, num_patches_list |
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video_path = './examples/red-panda.mp4' |
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pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1) |
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pixel_values = pixel_values.to(torch.bfloat16).cuda() |
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video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))]) |
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question = video_prefix + 'What is the red panda doing?' |
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# Frame1: <image>\nFrame2: <image>\n...\nFrame8: <image>\n{question} |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
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num_patches_list=num_patches_list, history=None, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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question = 'Describe this video in detail. Don\'t repeat.' |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, |
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num_patches_list=num_patches_list, history=history, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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``` |
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#### Streaming output |
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Besides this method, you can also use the following code to get streamed output. |
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```python |
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from transformers import TextIteratorStreamer |
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from threading import Thread |
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# Initialize the streamer |
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10) |
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# Define the generation configuration |
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generation_config = dict(max_new_tokens=1024, do_sample=False, streamer=streamer) |
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# Start the model chat in a separate thread |
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thread = Thread(target=model.chat, kwargs=dict( |
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tokenizer=tokenizer, pixel_values=pixel_values, question=question, |
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history=None, return_history=False, generation_config=generation_config, |
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)) |
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thread.start() |
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# Initialize an empty string to store the generated text |
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generated_text = '' |
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# Loop through the streamer to get the new text as it is generated |
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for new_text in streamer: |
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if new_text == model.conv_template.sep: |
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break |
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generated_text += new_text |
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print(new_text, end='', flush=True) # Print each new chunk of generated text on the same line |
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``` |
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## Finetune |
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Many repositories now support fine-tuning of the InternVL series models, including [InternVL](https://github.com/OpenGVLab/InternVL), [SWIFT](https://github.com/modelscope/ms-swift), [XTurner](https://github.com/InternLM/xtuner), and others. Please refer to their documentation for more details on fine-tuning. |
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## Deployment |
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### LMDeploy |
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LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. |
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```sh |
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pip install lmdeploy==0.5.3 |
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``` |
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LMDeploy abstracts the complex inference process of multi-modal Vision-Language Models (VLM) into an easy-to-use pipeline, similar to the Large Language Model (LLM) inference pipeline. |
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#### A 'Hello, world' example |
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```python |
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from lmdeploy import pipeline, TurbomindEngineConfig |
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from lmdeploy.vl import load_image |
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model = 'OpenGVLab/InternVL-Chat-V1-5' |
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image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg') |
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pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192)) |
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response = pipe(('describe this image', image)) |
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print(response.text) |
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``` |
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If `ImportError` occurs while executing this case, please install the required dependency packages as prompted. |
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#### Multi-images inference |
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When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased. |
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> Warning: Due to the scarcity of multi-image conversation data, the performance on multi-image tasks may be unstable, and it may require multiple attempts to achieve satisfactory results. |
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```python |
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from lmdeploy import pipeline, TurbomindEngineConfig |
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from lmdeploy.vl import load_image |
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from lmdeploy.vl.constants import IMAGE_TOKEN |
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model = 'OpenGVLab/InternVL-Chat-V1-5' |
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pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192)) |
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image_urls=[ |
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'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg', |
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'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg' |
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] |
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images = [load_image(img_url) for img_url in image_urls] |
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# Numbering images improves multi-image conversations |
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response = pipe((f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\ndescribe these two images', images)) |
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print(response.text) |
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``` |
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#### Batch prompts inference |
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Conducting inference with batch prompts is quite straightforward; just place them within a list structure: |
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|
|
```python |
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from lmdeploy import pipeline, TurbomindEngineConfig |
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from lmdeploy.vl import load_image |
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|
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model = 'OpenGVLab/InternVL-Chat-V1-5' |
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pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192)) |
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|
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image_urls=[ |
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"https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg", |
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"https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg" |
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] |
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prompts = [('describe this image', load_image(img_url)) for img_url in image_urls] |
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response = pipe(prompts) |
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print(response) |
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``` |
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#### Multi-turn conversation |
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|
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There are two ways to do the multi-turn conversations with the pipeline. One is to construct messages according to the format of OpenAI and use above introduced method, the other is to use the `pipeline.chat` interface. |
|
|
|
```python |
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from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig |
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from lmdeploy.vl import load_image |
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|
|
model = 'OpenGVLab/InternVL-Chat-V1-5' |
|
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192)) |
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|
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image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg') |
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gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8) |
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sess = pipe.chat(('describe this image', image), gen_config=gen_config) |
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print(sess.response.text) |
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sess = pipe.chat('What is the woman doing?', session=sess, gen_config=gen_config) |
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print(sess.response.text) |
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``` |
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|
#### Service |
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|
|
LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup: |
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|
|
```shell |
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lmdeploy serve api_server OpenGVLab/InternVL-Chat-V1-5 --backend turbomind --server-port 23333 |
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``` |
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|
|
To use the OpenAI-style interface, you need to install OpenAI: |
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|
|
```shell |
|
pip install openai |
|
``` |
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|
|
Then, use the code below to make the API call: |
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|
|
```python |
|
from openai import OpenAI |
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|
|
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1') |
|
model_name = client.models.list().data[0].id |
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response = client.chat.completions.create( |
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model=model_name, |
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messages=[{ |
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'role': |
|
'user', |
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'content': [{ |
|
'type': 'text', |
|
'text': 'describe this image', |
|
}, { |
|
'type': 'image_url', |
|
'image_url': { |
|
'url': |
|
'https://modelscope.oss-cn-beijing.aliyuncs.com/resource/tiger.jpeg', |
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}, |
|
}], |
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}], |
|
temperature=0.8, |
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top_p=0.8) |
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print(response) |
|
``` |
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## License |
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|
|
This project is released under the MIT license, while InternLM2 is licensed under the Apache-2.0 license. |
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## Citation |
|
|
|
If you find this project useful in your research, please consider citing: |
|
|
|
```BibTeX |
|
@article{chen2023internvl, |
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title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks}, |
|
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng}, |
|
journal={arXiv preprint arXiv:2312.14238}, |
|
year={2023} |
|
} |
|
@article{chen2024far, |
|
title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites}, |
|
author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others}, |
|
journal={arXiv preprint arXiv:2404.16821}, |
|
year={2024} |
|
} |
|
``` |
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|
