• 2022.4.28: Add support of inference on **Hugging Face transformers**. For how to use it, please refer to the doc [transformers.md](transformers.md) and our [Hugging Face models](https://huggingface.co/OFA-Sys).
• 2022.4.16: Released lightweight pretrained models **OFA-Medium** (~93M params) and **OFA-Tiny** (~33M params) in [checkpoints.md](checkpoints.md). To use them, you just need to load the corresponding checkpoint and set `--arch=ofa_medium` or `--arch=ofa_tiny` in the scripts.
• 2022.3.23: Added [Encouraging Loss](https://arxiv.org/pdf/2110.06537.pdf) as a feature. See [README_EncouragingLoss.md](README_EncouragingLoss.md). Leveraging this feature, OFA-Large has achieved improved results in both VQA (**test-std acc: 80.67**) and Image Classification (**test acc: 85.6**) recently.
• 2022.3.21: Released codes for pretraining OFA.
• 2022.3.18: Released the finetunedOFA-Base (~180M parameters) checkpoints and running scripts for vision & language tasks, including:Caption (146.4 CIDEr), VQA (78.07 on test-std), SNLI-VE (89.3 on dev), RefCOCO (90.67 on testA), RefCOCO+ (87.15 on testA) and RefCOCOg (82.31 on test-u).
• 2022.3.11: Released the finetuning & inference code/checkpoints forGigaword.
• 2022.3.08: Released the pretrained checkpoint ofOFA-Base incheckpoints.md. To use OFA-Base, you just need to loadofa_base.pt and change--arch=ofa_large to--arch=ofa_base in the training scripts.
• 2022.3.07: Released the finetuning & inference code/checkpoints forImage Classification, which achieves85.0 accuracy on ImageNet-1K, slightly better than reported in OFA paper.
• 2022.3.04: Released the finetuning & inference code/checkpoints forText-to-Image Generation.
• 2022.3.03: Released the finetuning & inference code/checkpoints forSNLI-VE andGLUE.
• 2022.2.22: Released the finetuning & inference code/checkpoints forVisual Question Answering, which can reproducethe reported VQA accuracy in OFA paper (80.02 on test-std). Check our results on theVQA Challenge.
• 2022.2.15: Released finetuning & inference code/checkpoints forReferring Expression Comprehension
• 2022.2.10: Released the inference code & finetuned checkpoint forImage captioning, which can reproducethe results on COCO Karparthy test split (149.6 CIDEr). OFA also achieves No.1 on the COCO image captioning online leaderboardLink (marked as M6-Team).

To pretrain OFA, you should first download the dataset we provide (pretrain_data_examples.zip, a small subset of the original pretraining data). For your customed pretraining datasets, please prepare your training samples into the same format.pretrain_data_examples.zip contains 4 TSV files:vision_language_examples.tsv,text_examples.tsv,image_examples.tsv anddetection_examples.tsv. Details of these files are as follows:

• vision_language_examples.tsv: Each line contains uniq-id, image (base64 string), caption, question, answer, ground-truth objects (objects appearing in the caption or question), dataset name (source of the data) and task type (caption, qa or visual gronunding). Prepared for the pretraining tasks of visual grounding, grounded captioning, image-text matching, image captioning and visual question answering.
• text_examples.tsv: Each line contains uniq-id and text. Prepared for the pretraining task of text infilling.
• image_examples.tsv: Each line contains uniq-id, image (base64 string, should be resized to 256*256 resolution) and image-code (generate the sparse codes for the central part of image through VQ-GAN). Prepared for the pretraining task of image infilling.
• detection_examples.tsv: Each line contains uniq-id, image (base64 string) and bounding box annotations (contains the top-left and bottom-right coordinates of the bounding box, object_id and object_name, seperated by commas). Prepared for the pretraining task of detection.

By default, the pretraining script will attempt to restore the released pretrained checkpoints of OFA-Base or OFA-Large and perform continuous pretraining. Continuous pretraining is more recommended, which achieves much better results compared with pretraining from scratch. For continuous pretraining, please download the pretrained weights in advance (seecheckpoints.md) and put them in the correct directoryOFA/checkpoints/. If not, the pretraining will begin from scratch.

cd run_scripts/pretrainingbash pretrain_ofa_large.sh # Pretrain OFA-Large. For OFA-Base, use pretrain_ofa_base.sh

If the pretrained OFA checkpoint is restored successfully, you will see the following information in the log:

INFO: Loaded checkpoint ../../checkpoints/ofa_large.pt

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. The dataset zipfilecaption_data.zip contains caption_stage1_train.tsv, caption_stage2_train.tsv, caption_val.tsv and caption_test.tsv. Each image corresponds to only 1 caption incaption_stage1_train.tsv and corresponds to multiple captions in other TSV files (about 5 captions per image). Each line of the dataset represents a caption sample with the following format. The information of uniq-id, image-id, caption, predicted object labels (taken fromVinVL, not used), image base64 string are separated by tabs.

162365  12455   the sun sets over the trees beyond some docks.  sky&&water&&dock&&pole  /9j/4AAQSkZJ....UCP/2Q==

Following previous standard practice, we divide the finetuning process of image captioning into two stages. In stage 1, we finetune OFA with cross-entropy loss on 4 NVIDIA-V100 GPUs with 32GB memory (expected to obtain ~139.5 CIDEr on the validation set at this stage). In stage 2, we select the best checkpoint of stage 1 and train with CIDEr optimization on 8 NVIDIA-V100 GPUs.Note that CIDEr optimization is very unstable and requires careful hyperparameter tuning. If you encounter training errors in the stage2 finetuning, you can increase the batch size or reduce the learning rate. If neither of these works, you can directly set--freeze-resnet to freeze the inner states of batch normalization.

cd run_scripts/captionnohup sh train_caption_stage1.sh > train_stage1.out &  # stage 1, train with cross-entropy lossnohup sh train_caption_stage2.sh > train_stage2.out &  # stage 2, load the best ckpt of stage1 and train with CIDEr optimization

Run the following commands to get your results and evaluate your model.

cd run_scripts/caption ; sh evaluate_caption.sh  # inference & evaluate

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. The dataset zipfilecoco_image_gen.zip containscoco_vqgan_train.tsv,coco_vqgan_dev.tsv andcoco_vqgan_full_test.tsv. Each line of the dataset represents a sample with the following format. The information of uniq-id, image-code (produced byvqgan, a list of integers separated by single-whitespaces), lowercased caption are separated by tabs.

16674 4336 4532 5334 3251 5461 3615 2469 ...4965 4190 1846the people are posing for a group photo.

The checkpoint zipfileimage_gen_large_best.zip containsimage_gen_large_best.pt,vqgan/last.ckpt,vqgan/model.yaml andclip/Vit-B-16.pt.

(Optional, but achieves better result): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance.

cd dataset/image_genln coco_vqgan_train.tsv coco_vqgan_train_1.tsvfor idx in `seq 1 9`;do shuf coco_vqgan_train_${idx}.tsv > coco_vqgan_train_$[${idx}+1].tsv;done # each file is used for an epoch

Following previous practice, we divide the finetuning process of image generating into two stages. In stage 1, we finetune OFA with cross-entropy loss on 4 8-V100-32G-GPU servers (expected to obtain ~32.5+ CLIP Score on the validation set at this stage). In stage 2, we select the last checkpoint of stage 1 and train with CLIP Score optimization on 4 8-V100-32G-GPU servers (expected to obtain ~34.0+ CLIP Score on the validation set at this stage). During the validation, the generated image will be dumped into_GEN_IMAGE_PATH_.

# run on each worker after the distributed and data configs have been correctly set following the guide in train_image_gen_stage1_distributed.sh cd run_scripts/image_gennohup sh train_image_gen_stage1_distributed.sh # stage 1, train with cross-entropy lossnohup sh train_image_gen_stage2_distributed.sh # stage 2, load the last ckpt of stage1 and train with CLIP Score optimization

Run the command below to generate your images.

cd run_scripts/image_gen ; sh evaluate_image_gen.sh  # inference & evaluate (FID, IS and CLIP Score)

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. The dataset zipfilevqa_data.zip is around 100G and the decompressed data costs around 135G disk storage, which contains the training, validation and testing samples together with other necessary data resources. (Sincevqa_data.zip is large in size, we have also provided chunked parts of the dataset files for more convenient and stable downloading. Please refer toissue #68.) Following common practice, VG-QA samples are also included in the training data. To adapt to the seq2seq paradigm of OFA, we transform original VQA training questions with multiple golden answers into multiple training samples. For the original VQA validation set, we keep around 10k samples for our validation and utilize the other samples for training. Each line of the dataset represents a VQA sample with the following format. The information of question-id, image-id, question, answer (with confidence), predicted object labels (taken fromVinVL, slightly brings around +0.1 accuracy improvement), image base64 string are separated by tabs.

79459   79459   is this person wearing shorts?  0.6|!+no    house&&short&&...&&sky  /9j/4AAQS...tigZ/9k=

For fine-tuning on customed VQA-formulated tasks, please refer to issue#76,#105 and#73 for more information.

(Optional, but achieves better finetuning accuracy): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance. In our experiments, we use shuffling which brings around+0.3 improvement on VQA accuracy.

cd dataset/vqa_dataln vqa_train.tsv vqa_train_1.tsvfor idx in `seq 1 9`;do shuf vqa_train_${idx}.tsv > vqa_train_$[${idx}+1].tsv;done # each file is used for an epoch

In our experiments, the VQA finetuning is performed on 4 8-A100-GPU servers (with RDMA). Here provides the finetuning scripttrain_vqa_distributed.sh, which supports multi-server distributed training (as well as single-server training). Please refer to the comments in the beginning of the script and set the configs correctly according to your distribution environment. If you have shuffled the training data in the previous step, please correctly specify the training data path following the guide in the script comments.The command should be run on each worker.

# run on each worker after the distributed and data configs have been correctly set following the guide in train_vqa_distributed.sh cd run_scripts/vqabash train_vqa_distributed.sh

In our experiments, the finetuning costs around 36 hours (for 12 epochs). After each epoch, an evaluation on validation set is performed. The best validation accuracy during finetuning will be around 80.8. The log is saved in${log_dir}.

(Update on validation time-cost) As will be mentioned in the4. Inference section, we prepare 2 types of inference: beam-search and all-candidate inference. By default, all-candidate inference is used for validation during fine-tuning, which achieves better accuracy but costs much time. Now we have added a new option in the training scripts called--val-inference-type to switch the validation inference type during fine-tuning. If you feel the validation takes too long, you can refer toPR #79 to activate beam-search validation, which significantly takes much less time, with around 0.5-0.6 validation score degradation compared with all-candidate validation.

We provide 2 types of inference,beam-search (much faster but gets sub-optimal accuracy) andall-candidate evaluation (slower but best accuracy).

For beam-search inference, use the scriptevaluate_vqa_beam.sh. Refer to the command below. The inference on test set costs around 16 GPU hours. After inference on test set, the result JSON file will be dumped in the${result_path} defined in the shell script. You can submit the resulttest_predict.json toEvalAI. Using our released finetuned checkpoint, beam-search inference will get 80.15 validation accuracy, 79.36 test-dev accuracy and 79.48 test-std accuracy (around 0.6 lower than all-candidate evaluation).

cd run_scripts/vqabash evaluate_vqa_beam.sh val # specify 'val' or 'test'

For all-candidate evaluation, we recommend to use the distributed scriptevaluate_vqa_allcand_distributed.sh. Please refer to the guide in the script to set the distributed configs before running. The result JSON file will be dumped in the${result_path} defined in the shell script of rank-0 server. All-candidate evaluation computes scores on all the candidate answers in the VQA dataset, which achieves80.82 validation accuracy,79.87 test-dev accuracy and80.02 test-std accuracy, reproducing our reported results in the paper. However, the inference on test set costs around 1k GPU hours, which is much slower.

# run on each worker after the distributed configs have been correctly set following the guide in evaluate_vqa_allcand_distributed.shcd run_scripts/vqabash evaluate_vqa_allcand_distributed.sh val # specify 'val' or 'test'

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. We provide RefCOCO (split by UNC), RefCOCO+ (split by UNC) and RefCOCOg (split by UMD) datasets. SeeRefCOCO andRefer for more details. Note that in the original dataset, each region-coord (or bounding box) may corresponds to multiple descriptive texts. We split these texts into multiple samples so that the region-coord in each sample corresponds to only one text. Each line of the processed dataset represents a sample with the following format. The information of uniq-id, image-id, text, region-coord (separated by commas), image base64 string are separated by tabs.

79_1    237367  A woman in a white blouse holding a glass of wine.  230.79,121.75,423.66,463.06 9j/4AAQ...1pAz/9k=

Unlike the original paper, we finetune OFA with a drop-path rate of 0.2, and found that training with this hyper-parameter achieves better results. We will update the reported results of the paper later.

cd run_scripts/refcoconohup sh train_refcoco.sh > train_refcoco.out &  # finetune for refcoconohup sh train_refcocoplus.sh > train_refcocoplus.out &  # finetune for refcoco+nohup sh train_refcocog.sh > train_refcocog.out &  # finetune for refcocog

Run the following commands for the evaluation.

cd run_scripts/refcoco ; sh evaluate_refcoco.sh  # inference & evaluate for refcoco/refcoco+/refcocog

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. Each line of the processed dataset represents a sample with the following format. The information of uniq-id, image-id, image base64 string, hypothesis, caption (or text premise), label are separated by tabs.

252244149.jpg#1r1n  252244149   /9j/4AAQ...MD/2Q==   a man in pink and gold is chewing on a wooden toothpick.   a man in pink is chewing a toothpick on the subway.   neutral

In our experiments, the SNLI-VE finetuning is performed on 8 NVIDIA-V100 GPUs with 32GB memory. In this task, we experimented with only a few sets of hyperparameters. We believe that proper hyperparameter tuning can lead to further accuracy improvement.

cd run_scripts/snli_venohup sh train_snli_ve.sh > train_snli_ve.out &  # finetune for snli_ve

Run the following command to obtain the results.

cd run_scripts/snli_ve ; sh evaluate_snli_ve.sh dev  # specify 'dev' or 'test'

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. we provide 7 language understanding datasets from GLUE benchmark, including COLA, MNLI, MRPC, QNLI, QQP, RTE and SST2. More details about these datasets can be found in thislink.

For each task, we have tried multiple sets of hyperparameters (including learning rate, batch size, training epochs). The results under different sets of hyperparameters can be found in${log_dir}.

cd run_scripts/gluenohup sh train_cola.sh > train_cola.out &  # finetune for colanohup sh train_mnli.sh > train_mnli.out &  # finetune for mnlinohup sh train_mrpc.sh > train_mrpc.out &  # finetune for mrpcnohup sh train_qnli.sh > train_qnli.out &  # finetune for qnlinohup sh train_qqp.sh > train_qqp.out &  # finetune for qqpnohup sh train_rte.sh > train_rte.out &  # finetune for rtenohup sh train_sst2.sh > train_sst2.out &  # finetune for sst2

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. Our provided data is derived from the originalImageNet-1K (ILSVRC2012 train & validation) dataset and shares the same data split with it. To formulate the classification task into seq2seq paradigm, we use thesynset words provided by Caffe as the generation target for each image class. Each line of the processed dataset represents a sample with the following format. The information of image base64 string, classification label (1-indexed, conform to the order insynset_words.txt), synset words of the label are separated by tabs.

_9j_4AAQS...fzX__Z  769 rugby ball

(Optional, but achieves better finetuning accuracy): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance. In our experiments, we use shuffling which brings around+0.2 improvement on ImageNet-1K accuracy.

cd dataset/imagenet_1k_dataln imagenet_1k_train.tsv imagenet_1k_train_1.tsvfor idx in `seq 1 9`;do shuf imagenet_1k_train_${idx}.tsv > imagenet_1k_train_$[${idx}+1].tsv;done # each file is used for an epoch one by one

In our experiments, the ImageNet-1K finetuning is performed on 2 8-A100-GPU servers (with RDMA). Here provides the finetuning scripttrain_imagenet_distributed.sh, which supports multi-server distributed training (as well as single-server training). Please refer to the comments in the beginning of the script and set the configs correctly according to your distribution environment. If you have shuffled the training data in the previous step, please correctly specify the training data path following the guide in the script comments.The command should be run on each worker. For quick evaluation during finetuning, by default we sample 20% of the original validation split and report accuracy on this subset after each epoch. The accuracy on the validation subset is generally ±0.1 relative to accuracy on the whole validation split.

# run on each worker after the distributed and data configs have been correctly set following the guide in train_imagenet_distributed.shcd run_scripts/image_classifybash train_imagenet_distributed.sh

In our experiments, the finetuning costs around 80 hours (for 32 epochs). The best accuracy on validation subset during finetuning will be around 85.0. The log is saved in${log_dir}.

To get the validation accuracy on the whole ImageNet-1K validation set, run the following command. The evaluation costs around 10 GPU hours. The accuracy will be reported in the stdout (expected to be around85.0).

cd run_scripts/image_classify ; sh evaluate_imagenet.sh  # inference & evaluate for imagenet-1k

Download data (seedatasets.md) and models (seecheckpoints.md) and put them in the correct directory. The original dataset is taken fromUniLM and we organized the data into the tsv format. Each line of the processed dataset represents a sample with the following format. The information of source and target texts are separated by tabs.

factory orders for manufactured goods rose #.# percent in september...  us september factory orders up #.# percent

Run the following command to train the model.

cd run_scripts/gigawordnohup sh train_gigaword.sh > train_gigaword.out &  # finetune for gigaword

Run the following command to obtain the results (~36.43 rougeL).

cd run_scripts/gigaword ; sh evaluate_gigaword.sh  # inference & evaluate for gigaword