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license: apache-2.0 |
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--- |
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# OFA-base |
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This is the **base** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-sequence learning framework. |
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The directory includes 4 files, namely `config.json` which consists of model configuration, `vocab.json` and `merge.txt` for our OFA tokenizer, and lastly `pytorch_model.bin` which consists of model weights. There is no need to worry about the mismatch between Fairseq and transformers, since we have addressed the issue yet. |
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To use it in transformers, please refer to https://github.com/OFA-Sys/OFA/tree/feature/add_transformers. Install the transformers and download the models as shown below. |
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``` |
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git clone --single-branch --branch feature/add_transformers https://github.com/OFA-Sys/OFA.git |
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pip install OFA/transformers/ |
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git clone https://huggingface.co/OFA-Sys/OFA-base |
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``` |
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After, refer the path to OFA-base to `ckpt_dir`, and prepare an image for the testing example below. Also, ensure that you have pillow and torchvision in your environment. |
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``` |
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>>> from PIL import Image |
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>>> from torchvision import transforms |
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>>> from transformers import OFATokenizer, OFAForConditionalGeneration |
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>>> mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] |
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>>> resolution = 384 |
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>>> patch_resize_transform = transforms.Compose([ |
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lambda image: image.convert("RGB"), |
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transforms.Resize((resolution, resolution), interpolation=Image.BICUBIC), |
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transforms.ToTensor(), |
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transforms.Normalize(mean=mean, std=std) |
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]) |
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>>> model = OFAForConditionalGeneration.from_pretrained(ckpt_dir) |
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>>> tokenizer = OFATokenizer.from_pretrained(ckpt_dir) |
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>>> txt = " what is the description of the image?" |
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>>> inputs = tokenizer([txt], max_length=1024, return_tensors="pt")["input_ids"] |
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>>> img = Image.open(path_to_image) |
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>>> patch_img = patch_resize_transform(img).unsqueeze(0) |
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>>> gen = model.generate(inputs, patch_images=patch_img, num_beams=4) |
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>>> print(tokenizer.batch_decode(gen, skip_special_tokens=True)) |
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``` |
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