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license: apache-2.0 |
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--- |
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# OFA-huge-caption |
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This is the **huge** version of OFA pretrained model finetuned on COCO captioning task, forked & converted from the [original fairseq version](https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_huge_best.pt) and compressed into float16. |
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The conversion script is custom, but the procedure described [Issue #171](https://github.com/OFA-Sys/OFA/issues/171) should also apply (quantization is not performed, but that's trivial). |
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You will need a [OFA modified version of transformers](https://github.com/OFA-Sys/OFA/tree/feature/add_transformers) to use this model. No idea why it is still not in master. Tips: You can just copy-paste the `transformers` folder into your project and rename it, then monkey-patch the `transformers` module to point to your local copy to avoid having to install it. |
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## Original README below |
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## Introduction |
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This is the **huge** 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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## How to use |
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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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```bash |
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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-huge |
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``` |
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After, refer the path to OFA-huge 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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```python |
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>>> from PIL import Image |
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>>> from torchvision import transforms |
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>>> from transformers import OFATokenizer, OFAModel |
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>>> from generate import sequence_generator |
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>>> mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] |
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>>> resolution = 480 |
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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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>>> tokenizer = OFATokenizer.from_pretrained(ckpt_dir) |
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>>> txt = " what does the image describe?" |
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>>> inputs = tokenizer([txt], 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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# using the generator of fairseq version |
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>>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=True) |
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>>> generator = sequence_generator.SequenceGenerator( |
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tokenizer=tokenizer, |
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beam_size=5, |
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max_len_b=16, |
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min_len=0, |
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no_repeat_ngram_size=3, |
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) |
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>>> data = {} |
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>>> data["net_input"] = {"input_ids": inputs, 'patch_images': patch_img, 'patch_masks':torch.tensor([True])} |
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>>> gen_output = generator.generate([model], data) |
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>>> gen = [gen_output[i][0]["tokens"] for i in range(len(gen_output))] |
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# using the generator of huggingface version |
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>>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=False) |
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>>> gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3) |
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>>> print(tokenizer.batch_decode(gen, skip_special_tokens=True)) |
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``` |
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