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  1. .gitattributes +0 -34
  2. .gitignore +3 -0
  3. EasyOCRLite +1 -0
  4. LICENSE +201 -0
  5. README.md +600 -13
  6. README_EncouragingLoss.md +34 -0
  7. app.py +197 -0
  8. checkpoints.md +37 -0
  9. checkpoints_cn.md +82 -0
  10. colab.md +9 -0
  11. criterions/__init__.py +4 -0
  12. criterions/clip_scst_loss.py +277 -0
  13. criterions/label_smoothed_cross_entropy.py +343 -0
  14. criterions/label_smoothed_encouraging_loss.py +395 -0
  15. criterions/scst_loss.py +281 -0
  16. data/__init__.py +0 -0
  17. data/cv_data/image_classify_dataset.py +196 -0
  18. data/data_utils.py +601 -0
  19. data/file_dataset.py +107 -0
  20. data/mm_data/__init__.py +0 -0
  21. data/mm_data/caption_dataset.py +160 -0
  22. data/mm_data/image_gen_dataset.py +171 -0
  23. data/mm_data/ocr_dataset.py +204 -0
  24. data/mm_data/refcoco_dataset.py +174 -0
  25. data/mm_data/snli_ve_dataset.py +203 -0
  26. data/mm_data/vqa_gen_dataset.py +218 -0
  27. data/nlg_data/summary_dataset.py +131 -0
  28. data/nlu_data/cola_dataset.py +138 -0
  29. data/nlu_data/mnli_dataset.py +143 -0
  30. data/nlu_data/mrpc_dataset.py +141 -0
  31. data/nlu_data/qnli_dataset.py +141 -0
  32. data/nlu_data/qqp_dataset.py +141 -0
  33. data/nlu_data/rte_dataset.py +141 -0
  34. data/nlu_data/sst2_dataset.py +138 -0
  35. data/ofa_dataset.py +79 -0
  36. data/pretrain_data/unify_dataset.py +636 -0
  37. datasets.md +44 -0
  38. evaluate.py +160 -0
  39. fairseq/.github/ISSUE_TEMPLATE.md +3 -0
  40. fairseq/.github/ISSUE_TEMPLATE/bug_report.md +43 -0
  41. fairseq/.github/ISSUE_TEMPLATE/documentation.md +15 -0
  42. fairseq/.github/ISSUE_TEMPLATE/feature_request.md +24 -0
  43. fairseq/.github/ISSUE_TEMPLATE/how-to-question.md +33 -0
  44. fairseq/.github/PULL_REQUEST_TEMPLATE.md +16 -0
  45. fairseq/.github/stale.yml +30 -0
  46. fairseq/.github/workflows/build.yml +55 -0
  47. fairseq/.github/workflows/build_wheels.yml +41 -0
  48. fairseq/.gitignore +136 -0
  49. fairseq/.gitmodules +4 -0
  50. fairseq/CODE_OF_CONDUCT.md +77 -0
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README.md CHANGED
@@ -1,13 +1,600 @@
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- ---
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- title: OFA OCR
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- emoji: 📉
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- colorFrom: gray
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- colorTo: blue
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- sdk: gradio
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- sdk_version: 3.9.1
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- app_file: app.py
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- pinned: false
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- license: apache-2.0
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <!---
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+ Copyright 2022 The OFA-Sys Team.
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+ All rights reserved.
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+ This source code is licensed under the Apache 2.0 license found in the LICENSE file in the root directory.
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+ -->
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+
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+ <p align="center">
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+ <br>
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+ <img src="examples/OFA_logo_tp_path.svg" width="150" />
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+ <br>
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+ <p>
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+ <br>
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+
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+ <p align="center">
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+ <a href="modelscope.md">ModelScope</a>&nbsp | &nbsp<a href="checkpoints.md">Checkpoints</a>&nbsp | &nbsp<a href="colab.md">Colab</a>&nbsp | &nbsp<a href="https://huggingface.co/ofa-sys">Demo</a>&nbsp | &nbsp<a href="http://arxiv.org/abs/2202.03052">Paper </a>&nbsp | &nbspBlog
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+ </p>
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+
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+ <p align="center">
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+ <br>
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+ <img src="examples/demo.gif" width="800" />
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+ <br>
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+ <p>
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+
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+ [colab]: <https://colab.research.google.com/assets/colab-badge.svg>
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+
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+ OFA is a unified sequence-to-sequence pretrained model (support **English** and **Chinese**) that unifies modalities (i.e., cross-modality, vision, language) and tasks (**finetuning** and **prompt tuning** are supported): image captioning (1st at the [MSCOCO Leaderboard](https://competitions.codalab.org/competitions/3221#results)), VQA ([link](https://eval.ai/web/challenges/challenge-page/830/leaderboard/2278)), visual grounding, text-to-image generation, text classification, text generation, image classification, etc. We provide **step-by-step** instructions for pretraining and finetuning and corresponding checkpoints (check official ckpt \[[EN](checkpoints.md)|[CN](checkpoints_cn.md)\] or [huggingface ckpt](https://huggingface.co/OFA-Sys)).
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+
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+ We sincerely welcome contributions to our project. Feel free to contact us or send us issues / PRs!
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+ <br></br>
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+
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+
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+ # Online Demos
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+ We provide online demo via Hugging Face Spaces for you to interact with our pretrained and finetuned models. Below are the links to the demos:
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+ * Image Captioning \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_image-caption_coco_large_en/summary) | [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Image_Caption)\]
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+ * Visual Grounding \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_visual-grounding_refcoco_large_en/summary) | [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Visual_Grounding)\]
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+ * Visual Question Answering \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_visual-question-answering_pretrain_large_en/summary) | [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Visual_Question_Answering)\]
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+ * Text-to-Image Generation \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_text-to-image-synthesis_coco_large_en/summary) | [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Text2Image_Generation)\]
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+ * Generic Interface \[[Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Generic_Interface)\]
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+
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+ Also we provide Colab notebooks for you to better perceive the procedures. Click [here](colab.md) to check them out!
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+ <br></br>
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+
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+ # Use in Huggingface Transformers
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+ We support the inference of OFA in Huggingface Transformers. Check the [README](transformers.md) and [Colab Notebook](https://colab.research.google.com/drive/1Ho81RBV8jysZ7e0FhsSCk_v938QeDuy3?usp=sharing) for more information. Codes are released in this branch https://github.com/OFA-Sys/OFA/tree/feature/add_transformers
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+ <br><br>
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+
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+
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+ # News
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+ * 2022.8.16: Released the **Chinese** version of OFA. **OFA-CN** needs only switching to `bpe_dir=../../utils/BERT_CN_dict` and `bpe=bert` and using our provided Chinese checkpoints in [checkpoints_cn.md](checkpoints_cn.md). Temporarily, we only provide base-size and large-size pretrained checkpoints and finetuned checkpoints on [MUGE Caption](https://tianchi.aliyun.com/muge) and the Chinese version of RefCOCO(-/+/g) (to release soon).
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+ * 2022.8.5: Released support of **prompt tuning** for OFA. Check our paper [here](https://arxiv.org/abs/2208.02532)! Please see the [prompt_tuning.md](prompt_tuning.md) for further details.
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+ * 2022.7.7: Updated support of OFA on **huggingface transformers** (fixed bugs in forward, add sequence generator from Fairseq to ensure performance, etc.). Refer to the doc [transformers.md](transformers.md) and the branch `feature/add_transformers`.
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+ * 2022.6.17: Released the pretrained checkpoint of **OFA-Huge**. To use it, set `--arch=ofa_huge` in the script.
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+ * 2022.5.15: OFA was accepted by **ICML 2022**
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+ * 2022.4.28: Add support of inference on **huggingface transformers**. For how to use it, please refer to the doc [transformers.md](transformers.md) and our [huggingface models](https://huggingface.co/OFA-Sys).
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+ * 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.
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+
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+ <details>
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+ <summary><b>More News</b></summary>
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+ <p>
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+ <ul>
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+ <li>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.</li>
62
+ <li>2022.3.21: Released codes for pretraining OFA.</li>
63
+ <li>2022.3.18: Released the finetuned <b>OFA-Base</b> (~180M parameters) checkpoints and running scripts for vision & language tasks, including: <b>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)</b>.</li>
64
+ <li>2022.3.11: Released the finetuning & inference code/checkpoints for <b>Gigaword</b>.</li>
65
+ <li>2022.3.08: Released the pretrained checkpoint of <b>OFA-Base</b> in <a href="https://github.com/OFA-Sys/OFA/blob/main/checkpoints.md">checkpoints.md</a>. To use OFA-Base, you just need to load <code>ofa_base.pt</code> and change <code>--arch=ofa_large</code> to <code>--arch=ofa_base</code> in the training scripts.</li>
66
+ <li>2022.3.07: Released the finetuning & inference code/checkpoints for <b>Image Classification</b>, which achieves <b>85.0</b> accuracy on ImageNet-1K, slightly better than reported in OFA paper.</li>
67
+ <li>2022.3.04: Released the finetuning & inference code/checkpoints for <b>Text-to-Image Generation</b>.</li>
68
+ <li>2022.3.03: Released the finetuning & inference code/checkpoints for <b>SNLI-VE</b> and <b>GLUE</b>.</li>
69
+ <li>2022.2.22: Released the finetuning & inference code/checkpoints for <b>Visual Question Answering</b>, which can reproduce <b>the reported VQA accuracy in OFA paper (80.02 on test-std)</b>. Check our results on the <a href="https://eval.ai/web/challenges/challenge-page/830/leaderboard/2278">VQA Challenge</a>.</li>
70
+ <li>2022.2.15: Released finetuning & inference code/checkpoints for <b>Referring Expression Comprehension</b></li>
71
+ <li>2022.2.10: Released the inference code & finetuned checkpoint for <b>Image captioning</b>, which can reproduce <b>the results on COCO Karparthy test split (149.6 CIDEr)</b>. OFA also achieves No.1 on the COCO image captioning online leaderboard <a href='https://competitions.codalab.org/competitions/3221#results'>Link</a> (marked as M6-Team).</li>
72
+ </ul>
73
+ </p>
74
+ </details>
75
+ <br></br>
76
+
77
+
78
+ # Model Card
79
+ We list the parameters and pretrained checkpoints of OFAs below. For finetuned checkpoints, please refer to [checkpoints.md](checkpoints.md).
80
+
81
+ <table border="1" width="100%">
82
+ <tr align="center">
83
+ <th>Model</th><th>Ckpt</th><th>Params</th><th>Backbone</th><th>Hidden size</th><th>Intermediate size</th><th>Num. of heads</th><th>Enc layers</th><th>Dec layers</th>
84
+ </tr>
85
+ <tr align="center">
86
+ <td>OFA<sub>Tiny</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_tiny.pt">Download</a></td><td>33M</td><td>ResNet50</td><td>256</td><td>1024</td><td>4</td><td>4</td><td>4</td>
87
+ </tr>
88
+ <tr align="center">
89
+ <td>OFA<sub>Medium</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_medium.pt">Download</a></td><td>93M</td><td>ResNet101</td><td>512</td></td><td>2048</td><td>8</td><td>4</td><td>4</td>
90
+ </tr>
91
+ <tr align="center">
92
+ <td>OFA<sub>Base</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_base.pt">Download</a></td><td>180M</td><td>ResNet101</td><td>768</td></td><td>3072</td><td>12</td><td>6</td><td>6</td>
93
+ </tr>
94
+ <tr align="center">
95
+ <td>OFA<sub>Large</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_large.pt">Download</a></td><td>470M</td><td>ResNet152</td><td>1024</td></td><td>4096</td><td>16</td><td>12</td><td>12</td>
96
+ </tr>
97
+ <tr align="center">
98
+ <td>OFA<sub>Huge</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_huge.pt">Download</a></td><td>930M</td><td>ResNet152</td><td>1280</td></td><td>5120</td><td>16</td><td>24</td><td>12</td>
99
+ </tr>
100
+ </table>
101
+ <br></br>
102
+
103
+ # Results
104
+ Below we demonstrate the results of OFAs on cross-modal understanding and generation.
105
+
106
+ <table border="1" width="100%">
107
+ <tr align="center">
108
+ <th>Task</th><th>Image Captioning</th><th>VQA</th><th>Visual Entailment</th><th colspan="3">Referring Expression Comprehension</th>
109
+ </tr>
110
+ <tr align="center">
111
+ <td>Dataset</td><td>COCO</td><td>VQA v2</td><td>SNLI-VE</td><td>RefCOCO</td><td>RefCOCO+</td><td>RefCOCOg</td>
112
+ </tr>
113
+ <tr align="center">
114
+ <td>Split</td><td>Karpathy test (CE/CIDEr)</td><td>test-dev/test-std</td><td>val/test</td><td>val/test-a/test-b</td><td>val/test-a/test-b</td><td>val-u/test-u</td>
115
+ </tr>
116
+ <tr align="center">
117
+ <td>Metric</td><td>CIDEr</td><td>Acc.</td><td>Acc.</td><td colspan="3">Acc.</td>
118
+ </tr>
119
+ <tr align="center">
120
+ <td>OFA<sub>Tiny</sub></td><td>119.0 / 128.7</td><td>70.3 / 70.4</td><td>85.3 / 85.2</td><td>80.20 / 84.07 / 75.00</td><td>68.22 / 75.13 / 57.66</td><td>72.02 / 69.74</td>
121
+ </tr>
122
+ <tr align="center">
123
+ <td>OFA<sub>Medium</sub></td><td>130.4 / 140.3</td><td>75.4 / 75.5</td><td>86.6 / 87.0</td><td>85.34 / 87.68 / 77.92</td><td>76.09 / 83.04 / 66.25</td><td>78.76 / 78.58</td>
124
+ </tr>
125
+ <tr align="center">
126
+ <td>OFA<sub>Base</sub></td><td>138.2 / 146.7</td><td>78.0 / 78.1</td><td>89.3 / 89.2</td><td>88.48 / 90.67 / 83.30</td><td>81.39 / 87.15 / 74.29</td><td>82.29 / 82.31</td>
127
+ </tr>
128
+ <tr align="center">
129
+ <td>OFA<sub>Large</sub></td><td>142.2 / 150.7</td><td>80.4 / 80.7</td><td>90.3 / 90.2</td><td>90.05 / 92.93 / 85.26</td><td>85.80 / 89.87 / 79.22</td><td>85.89 / 86.55</td>
130
+ </tr>
131
+ <tr align="center">
132
+ <td>OFA<sub>Huge</sub></td><td>145.3 / 154.9</td><td>82.0 / 82.0</td><td>91.0 / 91.2</td><td>92.04 / 94.03 / 88.44</td><td>87.86 / 91.70 / 80.71</td><td>88.07 / 88.78</td>
133
+ </tr>
134
+ </table>
135
+ <br></br>
136
+
137
+ # Requirements
138
+ * python 3.7.4
139
+ * pytorch 1.8.1
140
+ * torchvision 0.9.1
141
+ * JAVA 1.8 (for COCO evaluation)
142
+ <br></br>
143
+
144
+ # Installation
145
+ ```bash
146
+ git clone https://github.com/OFA-Sys/OFA
147
+ pip install -r requirements.txt
148
+ ```
149
+ <br></br>
150
+
151
+ # Datasets and Checkpoints
152
+ See [datasets.md](datasets.md) and [checkpoints.md](checkpoints.md).
153
+ <br></br>
154
+
155
+ # Training & Inference
156
+ Below we provide methods for training and inference on different tasks. We provide both pretrained OFA-Large and OFA-Base in [checkpoints.md](checkpoints.md). The scripts mentioned in this section are prepared for OFA-Large. For reproducing the downstreaming results of OFA-Base, we have also provided the corresponding finetuning and inference scripts for OFA-Base in the `run_scripts/` folder.
157
+
158
+ We recommend that your workspace directory should be organized like this:
159
+ ```
160
+ OFA/
161
+ ├── checkpoints/
162
+ │   ├── ofa_base.pt
163
+ │   ├── ofa_large.pt
164
+ │   ├── caption_large_best_clean.pt
165
+ │   └── ...
166
+ ├── criterions/
167
+ ├── data/
168
+ ├── dataset/
169
+ │   ├── caption_data/
170
+ │   ├── gigaword_data/
171
+ │   └── ...
172
+ ├── fairseq/
173
+ ├── models/
174
+ ├── run_scripts/
175
+ ├── tasks/
176
+ ├── train.py
177
+ ├── trainer.py
178
+ └── utils/
179
+ ```
180
+
181
+
182
+ ## Image Processing
183
+ To ensure the efficiency of processing data, we did not store images with small files, but instead we encode them to base64 strings.
184
+ Transforming image files to base64 strings is simple. Run the following code:
185
+ ```python
186
+ from PIL import Image
187
+ from io import BytesIO
188
+ import base64
189
+
190
+ img = Image.open(file_name) # path to file
191
+ img_buffer = BytesIO()
192
+ img.save(img_buffer, format=img.format)
193
+ byte_data = img_buffer.getvalue()
194
+ base64_str = base64.b64encode(byte_data) # bytes
195
+ base64_str = base64_str.decode("utf-8") # str
196
+ ```
197
+
198
+ ## Pretraining
199
+ Below we provide methods for pretraining OFA.
200
+
201
+ <details>
202
+ <summary><b>1. Prepare the Dataset</b></summary>
203
+ <p>
204
+ To pretrain OFA, you should first download the dataset we provide (<a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/pretrain_data/pretrain_data_examples.zip">pretrain_data_examples.zip</a>, a small subset of the original pretraining data). For your customed pretraining datasets, please prepare your training samples into the same format. <code>pretrain_data_examples.zip</code> contains 4 TSV files: <code>vision_language_examples.tsv</code>, <code>text_examples.tsv</code>, <code>image_examples.tsv</code> and <code>detection_examples.tsv</code>. Details of these files are as follows:
205
+ <br />
206
+ <ul type="circle">
207
+ <li><b>vision_language_examples.tsv</b>:
208
+ 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. </li>
209
+ <li><b>text_examples.tsv</b>: Each line contains uniq-id and text. Prepared for the pretraining task of text infilling. </li>
210
+ <li><b>image_examples.tsv</b>: 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. </li>
211
+ <li><b>detection_examples.tsv</b>: 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. </li>
212
+ </ul>
213
+ In addition, the folder negative_sample in pretrain_data_examples.zip contains three files <code>all_captions.txt</code>, <code>object.txt</code> and <code>type2ans.json</code>. The data in these files are used as negative samples for the image-text matching (ITM) task.
214
+ </p>
215
+ </details>
216
+ <details>
217
+ <summary><b>2. Pretraining</b></summary>
218
+ <p>
219
+ 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 (see <a href='checkpoints.md'>checkpoints.md</a>) and put them in the correct directory <code>OFA/checkpoints/</code>. If not, the pretraining will begin from scratch.
220
+ </p>
221
+ <pre>
222
+ cd run_scripts/pretraining
223
+ bash pretrain_ofa_large.sh # Pretrain OFA-Large. For OFA-Base, use pretrain_ofa_base.sh
224
+ </pre>
225
+ <p>
226
+ If the pretrained OFA checkpoint is restored successfully, you will see the following information in the log:
227
+ </p>
228
+ <pre>
229
+ INFO: Loaded checkpoint ../../checkpoints/ofa_large.pt
230
+ </pre>
231
+ </details>
232
+
233
+ ## Image Captioning
234
+ We provide procedures to reproduce our results of image captioning on our paper below.
235
+ <details>
236
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
237
+ <p>
238
+ Download data (see <a href='datasets.md'>datasets.md</a>) and models (see <a href='checkpoints.md'>checkpoints.md</a>) and put them in the correct directory. The dataset zipfile <code>caption_data.zip</code> contains caption_stage1_train.tsv, caption_stage2_train.tsv, caption_val.tsv and caption_test.tsv. Each image corresponds to only 1 caption in <code>caption_stage1_train.tsv</code> 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 from <a href='https://github.com/pzzhang/VinVL'>VinVL</a>, not used), image base64 string are separated by tabs.
239
+ </p>
240
+ <pre>
241
+ 162365 12455 the sun sets over the trees beyond some docks. sky&&water&&dock&&pole /9j/4AAQSkZJ....UCP/2Q==
242
+ </pre>
243
+ </details>
244
+ <details>
245
+ <summary><b>2. Finetuning</b></summary>
246
+ <p>
247
+ 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. <b>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 </b><code>--freeze-resnet</code><b> to freeze the inner states of batch normalization.</b>
248
+ </p>
249
+ <pre>
250
+ cd run_scripts/caption
251
+ nohup sh train_caption_stage1.sh > train_stage1.out & # stage 1, train with cross-entropy loss
252
+ nohup sh train_caption_stage2.sh > train_stage2.out & # stage 2, load the best ckpt of stage1 and train with CIDEr optimization
253
+ </pre>
254
+ </details>
255
+ <details>
256
+ <summary><b>3. Inference</b></summary>
257
+ <p>
258
+ Run the following commands to get your results and evaluate your model.
259
+ </p>
260
+ <pre>
261
+ cd run_scripts/caption ; sh evaluate_caption.sh # inference & evaluate
262
+ </pre>
263
+ </details>
264
+
265
+ ## Text-to-Image Generation
266
+ This part provides procedures for the finetuning and inference of text-to-image generation. See below.
267
+
268
+ <details>
269
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
270
+ <p>
271
+ Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. The dataset zipfile <code>coco_image_gen.zip</code> contains <code>coco_vqgan_train.tsv</code>, <code>coco_vqgan_dev.tsv</code> and <code>coco_vqgan_full_test.tsv</code>. Each line of the dataset represents a sample with the following format. The information of uniq-id, image-code (produced by <a href="https://github.com/CompVis/taming-transformers">vqgan</a>, a list of integers separated by single-whitespaces), lowercased caption are separated by tabs.
272
+ </p>
273
+ <pre>
274
+ 1 6674 4336 4532 5334 3251 5461 3615 2469 ...4965 4190 1846 the people are posing for a group photo.
275
+ </pre>
276
+ <p>
277
+ The checkpoint zipfile <code>image_gen_large_best.zip</code> contains <code>image_gen_large_best.pt</code>, <code>vqgan/last.ckpt</code>, <code>vqgan/model.yaml</code> and <code>clip/Vit-B-16.pt</code>.
278
+ </p>
279
+ </details>
280
+ <details>
281
+ <summary><b>2. Shuffle the Training Data</b></summary>
282
+ <p>
283
+ (Optional, but achieves better result): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance.
284
+ </p>
285
+ <pre>
286
+ cd dataset/image_gen
287
+ ln coco_vqgan_train.tsv coco_vqgan_train_1.tsv
288
+ for 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
289
+ </pre>
290
+ </details>
291
+ <details>
292
+ <summary><b>3. Finetuning</b></summary>
293
+ <p>
294
+ 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 <code>_GEN_IMAGE_PATH_</code>.
295
+ </p>
296
+ <pre>
297
+ # run on each worker after the distributed and data configs have been correctly set following the guide in train_image_gen_stage1_distributed.sh
298
+ cd run_scripts/image_gen
299
+ nohup sh train_image_gen_stage1_distributed.sh # stage 1, train with cross-entropy loss
300
+ nohup sh train_image_gen_stage2_distributed.sh # stage 2, load the last ckpt of stage1 and train with CLIP Score optimization
301
+ </pre>
302
+ </details>
303
+ <details>
304
+ <summary><b>4. Inference</b></summary>
305
+ <p>
306
+ Run the command below to generate your images.
307
+ </p>
308
+ <pre>
309
+ cd run_scripts/image_gen ; sh evaluate_image_gen.sh # inference & evaluate (FID, IS and CLIP Score)
310
+ </pre>
311
+ </details>
312
+
313
+ ## Visual Question Answering
314
+ Here we provide the finetuning and inference codes to reproduce the VQAv2 result reported in our paper (**test-std 80.02**). We believe much improvement on accuracy can still be achieved based on this codebase :)
315
+ <details>
316
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
317
+ <p>
318
+ Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. The dataset zipfile <code>vqa_data.zip</code> 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. (Since <code>vqa_data.zip</code> is large in size, we have also provided chunked parts of the dataset files for more convenient and stable downloading. Please refer to <a href="https://github.com/OFA-Sys/OFA/issues/68#issuecomment-1096837349">issue #68</a>.) 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 from <a href="https://github.com/pzzhang/VinVL">VinVL</a>, slightly brings around +0.1 accuracy improvement), image base64 string are separated by tabs.
319
+ </p>
320
+ <pre>
321
+ 79459 79459 is this person wearing shorts? 0.6|!+no house&&short&&...&&sky /9j/4AAQS...tigZ/9k=
322
+ </pre>
323
+ <p>
324
+ For fine-tuning on customed VQA-formulated tasks, please refer to issue <a href="https://github.com/OFA-Sys/OFA/issues/76">#76</a>, <a href="https://github.com/OFA-Sys/OFA/issues/105">#105</a> and <a href="https://github.com/OFA-Sys/OFA/issues/73">#73</a> for more information.
325
+ </p>
326
+ </details>
327
+ <details>
328
+ <summary><b>2. Shuffle the Training Data</b></summary>
329
+ <p>
330
+ (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 <b>+0.3</b> improvement on VQA accuracy.
331
+ </p>
332
+ <pre>
333
+ cd dataset/vqa_data
334
+ ln vqa_train.tsv vqa_train_1.tsv
335
+ for idx in `seq 1 9`;do shuf vqa_train_${idx}.tsv > vqa_train_$[${idx}+1].tsv;done # each file is used for an epoch
336
+ </pre>
337
+ </details>
338
+ <details>
339
+ <summary><b>3. Finetuning</b></summary>
340
+ <p>
341
+ In our experiments, the VQA finetuning is performed on 4 8-A100-GPU servers (<i>with RDMA</i>). Here provides the finetuning script <code>train_vqa_distributed.sh</code>, 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. <b>The command should be run on each worker.</b>
342
+ </p>
343
+ <pre>
344
+ # run on each worker after the distributed and data configs have been correctly set following the guide in train_vqa_distributed.sh
345
+ cd run_scripts/vqa
346
+ bash train_vqa_distributed.sh
347
+ </pre>
348
+ <p>
349
+ 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 <code>${log_dir}</code>.
350
+ </p>
351
+ <p>
352
+ <i>(Update on validation time-cost)</i> As will be mentioned in the <i>4. Inference</i> section, we prepare 2 types of inference: beam-search and all-candidate inference. By default, all-candidate inference is used for validation during fine-tuning, which achieves better accuracy but costs much time. Now we have added a new option in the training scripts called <code>--val-inference-type</code> to switch the validation inference type during fine-tuning. If you feel the validation takes too long, you can refer to <a href="https://github.com/OFA-Sys/OFA/pull/79">PR #79</a> to activate beam-search validation, which significantly takes much less time, with around 0.5-0.6 validation score degradation compared with all-candidate validation.
353
+ </p>
354
+ </details>
355
+ <details>
356
+ <summary><b>4. Inference</b></summary>
357
+ <p>
358
+ We provide 2 types of inference, <b>beam-search</b> (much faster but gets sub-optimal accuracy) and <b>all-candidate evaluation</b> (slower but best accuracy). <br></br>
359
+ For beam-search inference, use the script <code>evaluate_vqa_beam.sh</code>. Refer to the command below. The inference on test set costs around 16 GPU hours. After inference on test set, the result JSON file will be dumped in the <code>${result_path}</code> defined in the shell script. You can submit the result <code>test_predict.json</code> to <a href="https://eval.ai/web/challenges/challenge-page/830/overview">EvalAI</a>. 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).
360
+ </p>
361
+ <pre>
362
+ cd run_scripts/vqa
363
+ bash evaluate_vqa_beam.sh val # specify 'val' or 'test'
364
+ </pre>
365
+ <p>
366
+ For all-candidate evaluation, we recommend to use the distributed script <code>evaluate_vqa_allcand_distributed.sh</code>. Please refer to the guide in the script to set the distributed configs before running. The result JSON file will be dumped in the <code>${result_path}</code> defined in the shell script of rank-0 server. All-candidate evaluation computes scores on all the candidate answers in the VQA dataset, which achieves <b>80.82</b> validation accuracy, <b>79.87</b> test-dev accuracy and <b>80.02</b> 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.
367
+ </p>
368
+ <pre>
369
+ # run on each worker after the distributed configs have been correctly set following the guide in evaluate_vqa_allcand_distributed.sh
370
+ cd run_scripts/vqa
371
+ bash evaluate_vqa_allcand_distributed.sh val # specify 'val' or 'test'
372
+ </pre>
373
+ </details>
374
+
375
+ ## Visual Grounding (Referring Expression Comprehension)
376
+ Here provides procedures for you to prepare data, train, and evaluate your model on visual grounding.
377
+ <details>
378
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
379
+ <p>
380
+ Download data (see <a href='datasets.md'>datasets.md</a>) and models (see <a href='checkpoints.md'>checkpoints.md</a>) and put them in the correct directory. We provide RefCOCO (split by UNC), RefCOCO+ (split by UNC) and RefCOCOg (split by UMD) datasets. See <a href='https://www.tensorflow.org/datasets/catalog/ref_coco'>RefCOCO</a> and <a href="https://github.com/lichengunc/refer">Refer</a> 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.
381
+ </p>
382
+ <pre>
383
+ 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=
384
+ </pre>
385
+ </details>
386
+ <details>
387
+ <summary><b>2. Finetuning</b></summary>
388
+ <p>
389
+ 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.
390
+ </p>
391
+ <pre>
392
+ cd run_scripts/refcoco
393
+ nohup sh train_refcoco.sh > train_refcoco.out & # finetune for refcoco
394
+ nohup sh train_refcocoplus.sh > train_refcocoplus.out & # finetune for refcoco+
395
+ nohup sh train_refcocog.sh > train_refcocog.out & # finetune for refcocog
396
+ </pre>
397
+ </details>
398
+ <details>
399
+ <summary><b>3. Inference</b></summary>
400
+ <p>
401
+ Run the following commands for the evaluation.
402
+ </p>
403
+ <pre>
404
+ cd run_scripts/refcoco ; sh evaluate_refcoco.sh # inference & evaluate for refcoco/refcoco+/refcocog
405
+ </pre>
406
+ </details>
407
+
408
+ ## Visual Entailment
409
+ We provide steps for you to reproduce our results in visual entailment. See the details below.
410
+
411
+ <details>
412
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
413
+ <p>
414
+ Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) 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.
415
+ </p>
416
+ <pre>
417
+ 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
418
+ </pre>
419
+ </details>
420
+ <details>
421
+ <summary><b>2. Finetuning</b></summary>
422
+ <p>
423
+ 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.
424
+ </p>
425
+ <pre>
426
+ cd run_scripts/snli_ve
427
+ nohup sh train_snli_ve.sh > train_snli_ve.out & # finetune for snli_ve
428
+ </pre>
429
+ </details>
430
+ <details>
431
+ <summary><b>3. Inference</b></summary>
432
+ <p>
433
+ Run the following command to obtain the results.
434
+ </p>
435
+ <pre>
436
+ cd run_scripts/snli_ve ; sh evaluate_snli_ve.sh dev # specify 'dev' or 'test'
437
+ </pre>
438
+ </details>
439
+
440
+ ## GLUE
441
+ Here we provide steps for you to finetune and evaluate our model on language understanding tasks. We demonstrate our practice for the GLUE benchmark.
442
+
443
+ <details>
444
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
445
+ <p>
446
+ Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) 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 this <a href="https://openreview.net/pdf?id=rJ4km2R5t7">link</a>.
447
+ </p>
448
+ </details>
449
+ <details>
450
+ <summary><b>2. Finetuning</b></summary>
451
+ <p>
452
+ 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 <code>${log_dir}</code>.
453
+ </p>
454
+ <pre>
455
+ cd run_scripts/glue
456
+ nohup sh train_cola.sh > train_cola.out & # finetune for cola
457
+ nohup sh train_mnli.sh > train_mnli.out & # finetune for mnli
458
+ nohup sh train_mrpc.sh > train_mrpc.out & # finetune for mrpc
459
+ nohup sh train_qnli.sh > train_qnli.out & # finetune for qnli
460
+ nohup sh train_qqp.sh > train_qqp.out & # finetune for qqp
461
+ nohup sh train_rte.sh > train_rte.out & # finetune for rte
462
+ nohup sh train_sst2.sh > train_sst2.out & # finetune for sst2
463
+ </pre>
464
+ </details>
465
+
466
+ ## Image Classification on ImageNet-1K
467
+ We provide the finetuning and inference codes which reproduce **85.0 ImageNet-1K accuracy**, slightly better than reported in our paper.
468
+
469
+ <details>
470
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
471
+ <p>
472
+ Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. Our provided data is derived from the original <a href="http://image-net.org/">ImageNet-1K</a> (ILSVRC2012 train & validation) dataset and shares the same data split with it. To formulate the classification task into seq2seq paradigm, we use the <a href="https://github.com/HoldenCaulfieldRye/caffe/blob/master/data/ilsvrc12/synset_words.txt">synset words</a> 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 in <code>synset_words.txt</code>), synset words of the label are separated by tabs.
473
+ </p>
474
+ <pre>
475
+ _9j_4AAQS...fzX__Z 769 rugby ball
476
+ </pre>
477
+ </details>
478
+ <details>
479
+ <summary><b>2. Shuffle the Training Data</b></summary>
480
+ <p>
481
+ (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 <b>+0.2</b> improvement on ImageNet-1K accuracy.
482
+ </p>
483
+ <pre>
484
+ cd dataset/imagenet_1k_data
485
+ ln imagenet_1k_train.tsv imagenet_1k_train_1.tsv
486
+ for idx in `seq 1 9`;do shuf imagenet_1k_train_${idx}.tsv > imagenet_1k_train_$[${idx}+1].tsv;done # each file is used for an epoch one by one
487
+ </pre>
488
+ </details>
489
+ <details>
490
+ <summary><b>3. Finetuning</b></summary>
491
+ <p>
492
+ In our experiments, the ImageNet-1K finetuning is performed on 2 8-A100-GPU servers (<i>with RDMA</i>). Here provides the finetuning script <code>train_imagenet_distributed.sh</code>, which supports multi-server distributed training (as well as single-server training). Please refer to the comments in the beginning of the script and set the configs correctly according to your distribution environment. If you have shuffled the training data in the previous step, please correctly specify the training data path following the guide in the script comments. <b>The command should be run on each worker.</b> For quick evaluation during finetuning, by default we sample 20% of the original validation split and report accuracy on this subset after each epoch. The accuracy on the validation subset is generally ±0.1 relative to accuracy on the whole validation split.
493
+ </p>
494
+ <pre>
495
+ # run on each worker after the distributed and data configs have been correctly set following the guide in train_imagenet_distributed.sh
496
+ cd run_scripts/image_classify
497
+ bash train_imagenet_distributed.sh
498
+ </pre>
499
+ <p>
500
+ In our experiments, the finetuning costs around 80 hours (for 32 epochs). The best accuracy on validation subset during finetuning will be around 85.0. The log is saved in <code>${log_dir}</code>.
501
+ </p>
502
+ </details>
503
+ <details>
504
+ <summary><b>4. Inference</b></summary>
505
+ <p>
506
+ 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 around <b>85.0</b>).
507
+ </p>
508
+ <pre>
509
+ cd run_scripts/image_classify ; sh evaluate_imagenet.sh # inference & evaluate for imagenet-1k
510
+ </pre>
511
+ </details>
512
+
513
+ ## Gigaword
514
+ We provide steps for you to reproduce our results in Gigaword. See the details below.
515
+
516
+ <details>
517
+ <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
518
+ <p>
519
+ Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. The original dataset is taken from <a href="https://github.com/microsoft/unilm/">UniLM</a> 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.
520
+ </p>
521
+ <pre>
522
+ factory orders for manufactured goods rose #.# percent in september... us september factory orders up #.# percent
523
+ </pre>
524
+ </details>
525
+ <details>
526
+ <summary><b>2. Finetuning</b></summary>
527
+ <p>
528
+ Run the following command to train the model.
529
+ </p>
530
+ <pre>
531
+ cd run_scripts/gigaword
532
+ nohup sh train_gigaword.sh > train_gigaword.out & # finetune for gigaword
533
+ </pre>
534
+ </details>
535
+ <details>
536
+ <summary><b>3. Inference</b></summary>
537
+ <p>
538
+ Run the following command to obtain the results (~36.43 rougeL).
539
+ </p>
540
+ <pre>
541
+ cd run_scripts/gigaword ; sh evaluate_gigaword.sh # inference & evaluate for gigaword
542
+ </pre>
543
+ </details>
544
+
545
+ <br></br>
546
+
547
+ # Gallery
548
+ Below we provide examples of OFA in text-to-image generation and open-ended VQA. Also, we demonstrate its performance in unseen task (Grounded QA) as well as unseen domain (Visual Grounding on images from unseen domains).
549
+
550
+ ## Text-to-Image Generation
551
+
552
+ ![case1](examples/case1.png)
553
+
554
+
555
+ ## Open-Ended VQA
556
+ ![open_vqa](examples/open_vqa.png)
557
+
558
+ ## Grounded QA (unseen task)
559
+ ![grounded_qa](examples/grounded_qa.png)
560
+
561
+ ## Visual Grounding (unseen domain)
562
+ ![vg](examples/viusal_grounding.png)
563
+ <br></br>
564
+
565
+ # Related Codebase
566
+ * [Fairseq](https://github.com/pytorch/fairseq)
567
+ * [taming-transformers](https://github.com/CompVis/taming-transformers)
568
+ <br></br>
569
+
570
+
571
+ # Getting Involved
572
+ Feel free to submit Github issues or pull requests. Welcome to contribute to our project!
573
+
574
+ To contact us, never hestitate to send an email to `zheluo.wp@alibaba-inc.com` or `junyang.ljy@alibaba-inc.com`!
575
+ <br></br>
576
+
577
+
578
+ # Citation
579
+ Please cite our paper if you find it helpful :)
580
+
581
+ ```
582
+ @article{wang2022ofa,
583
+ author = {Peng Wang and
584
+ An Yang and
585
+ Rui Men and
586
+ Junyang Lin and
587
+ Shuai Bai and
588
+ Zhikang Li and
589
+ Jianxin Ma and
590
+ Chang Zhou and
591
+ Jingren Zhou and
592
+ Hongxia Yang},
593
+ title = {OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence
594
+ Learning Framework},
595
+ journal = {CoRR},
596
+ volume = {abs/2202.03052},
597
+ year = {2022}
598
+ }
599
+ ```
600
+ <br></br>
README_EncouragingLoss.md ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Finetuning with Encouraging Loss (EL)
2
+ Below we provide methods for finetuning with label smoothed encouraging loss proposed in [_Well-classified Examples are Underestimated in Classification with Deep Neural Networks_](https://arxiv.org/pdf/2110.06537.pdf) on different downstream tasks.
3
+ The implementation is in [label_smoothed_encouraging_loss.py](criterions/label_smoothed_encouraging_loss.py).
4
+ You can set the `--criterion` to `adjust_label_smoothed_encouraging_loss` to use it. This criterion has a hyper-parameter `--log-end`.
5
+ `--log-end < 1` results in a approximated and conservative version of the full encouraging loss.
6
+ A high log_end will more strongly weaken the gradient vanishing, enhance the modeling of the data, and increase the growth rate of the margin, but it will also bring a larger gradient norm, which will bring challenges to the existing optimization system.
7
+ We recommend higher log_end for cases with higher performance, and 0.75 or 0.5 as your first try.
8
+ ## Image Captioning
9
+ We provide procedures for image captioning with EL below. The preprocessing is identical to default setting.
10
+
11
+ <details>
12
+ <summary><b>Finetuning</b></summary>
13
+ <p>
14
+ We propose two scripts for stage1. </b>
15
+ </p>
16
+ <pre>
17
+ cd run_scripts/caption
18
+ nohup sh train_caption_stage1_el.sh > train_stage1_el.out & # stage 1, train with encouraging loss, expected cider 1.403
19
+ nohup sh train_caption_stage1_el_db.sh > train_stage1_el.out & # stage 1, train with encouraging loss, and drop best examples, expected cider 1.404
20
+ </pre>
21
+ </details>
22
+
23
+ ## Referring Expression Comprehension
24
+ We provide procedures for image captioning with EL below. The preprocessing is identical to default setting.
25
+ <details>
26
+ <summary><b>Finetuning</b></summary>
27
+ <pre>
28
+ cd run_scripts/refcoco
29
+ nohup sh train_refcoco_el.sh > train_refcoco_el.out & # finetune for refcoco
30
+ nohup sh train_refcocoplus_el.sh > train_refcocoplus_el.out & # finetune for refcoco+
31
+ nohup sh train_refcocog_el.sh > train_refcocog_el.out & # finetune for refcocog
32
+ </pre>
33
+ </details>
34
+ Evaluation is also the same as the default setting.
app.py ADDED
@@ -0,0 +1,197 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ os.system('cd fairseq;'
4
+ 'pip install --use-feature=in-tree-build ./; cd ..')
5
+ os.system('ls -l')
6
+
7
+ import torch
8
+ import numpy as np
9
+ from fairseq import utils, tasks
10
+ from fairseq import checkpoint_utils
11
+ from utils.eval_utils import eval_step
12
+ from data.mm_data.ocr_dataset import ocr_resize
13
+ from tasks.mm_tasks.ocr import OcrTask
14
+ from PIL import Image, ImageDraw
15
+ from torchvision import transforms
16
+ from typing import List, Tuple
17
+ import cv2
18
+ from easyocrlite import ReaderLite
19
+ import gradio as gr
20
+
21
+
22
+ # Register refcoco task
23
+ tasks.register_task('ocr', OcrTask)
24
+
25
+ os.system('wget http://xc-models.oss-cn-zhangjiakou.aliyuncs.com/ofa/chinese/ocr/general/checkpoint_last.pt; '
26
+ 'mkdir -p checkpoints; mv checkpoint_last.pt checkpoints/ocr.pt')
27
+
28
+ # turn on cuda if GPU is available
29
+ use_cuda = torch.cuda.is_available()
30
+ # use fp16 only when GPU is available
31
+ use_fp16 = False
32
+
33
+ mean = [0.5, 0.5, 0.5]
34
+ std = [0.5, 0.5, 0.5]
35
+
36
+ Rect = Tuple[int, int, int, int]
37
+ FourPoint = Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int], Tuple[int, int]]
38
+
39
+
40
+ def four_point_transform(image: np.ndarray, rect: FourPoint) -> np.ndarray:
41
+ (tl, tr, br, bl) = rect
42
+
43
+ widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
44
+ widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
45
+ maxWidth = max(int(widthA), int(widthB))
46
+
47
+ # compute the height of the new image, which will be the
48
+ # maximum distance between the top-right and bottom-right
49
+ # y-coordinates or the top-left and bottom-left y-coordinates
50
+ heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
51
+ heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
52
+ maxHeight = max(int(heightA), int(heightB))
53
+
54
+ dst = np.array(
55
+ [[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]],
56
+ dtype="float32",
57
+ )
58
+
59
+ # compute the perspective transform matrix and then apply it
60
+ M = cv2.getPerspectiveTransform(rect, dst)
61
+ warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
62
+
63
+ return warped
64
+
65
+
66
+ def get_images(image_path: str, reader: ReaderLite, **kwargs):
67
+ results = reader.process(image_path, **kwargs)
68
+ return results
69
+
70
+
71
+ def draw_boxes(image, bounds, color='red', width=2):
72
+ draw = ImageDraw.Draw(image)
73
+ for bound in bounds:
74
+ p0, p1, p2, p3 = bound
75
+ draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
76
+ return image
77
+
78
+
79
+ def encode_text(task, text, length=None, append_bos=False, append_eos=False):
80
+ bos_item = torch.LongTensor([task.src_dict.bos()])
81
+ eos_item = torch.LongTensor([task.src_dict.eos()])
82
+ pad_idx = task.src_dict.pad()
83
+
84
+ s = task.tgt_dict.encode_line(
85
+ line=task.bpe.encode(text),
86
+ add_if_not_exist=False,
87
+ append_eos=False
88
+ ).long()
89
+ if length is not None:
90
+ s = s[:length]
91
+ if append_bos:
92
+ s = torch.cat([bos_item, s])
93
+ if append_eos:
94
+ s = torch.cat([s, eos_item])
95
+ return s
96
+
97
+
98
+ def patch_resize_transform(patch_image_size=480, is_document=False):
99
+ _patch_resize_transform = transforms.Compose(
100
+ [
101
+ lambda image: ocr_resize(
102
+ image, patch_image_size, is_document=is_document
103
+ ),
104
+ transforms.ToTensor(),
105
+ transforms.Normalize(mean=mean, std=std),
106
+ ]
107
+ )
108
+
109
+ return _patch_resize_transform
110
+
111
+
112
+ # Construct input for caption task
113
+ def construct_sample(task, image: Image, patch_image_size=480):
114
+ bos_item = torch.LongTensor([task.src_dict.bos()])
115
+ eos_item = torch.LongTensor([task.src_dict.eos()])
116
+ pad_idx = task.src_dict.pad()
117
+
118
+ patch_image = patch_resize_transform(patch_image_size)(image).unsqueeze(0)
119
+ patch_mask = torch.tensor([True])
120
+ src_text = encode_text(task, "图片上的文字是什么?", append_bos=True, append_eos=True).unsqueeze(0)
121
+ src_length = torch.LongTensor([s.ne(pad_idx).long().sum() for s in src_text])
122
+ sample = {
123
+ "id":np.array(['42']),
124
+ "net_input": {
125
+ "src_tokens": src_text,
126
+ "src_lengths": src_length,
127
+ "patch_images": patch_image,
128
+ "patch_masks": patch_mask,
129
+ },
130
+ "target": None
131
+ }
132
+ return sample
133
+
134
+
135
+ # Function to turn FP32 to FP16
136
+ def apply_half(t):
137
+ if t.dtype is torch.float32:
138
+ return t.to(dtype=torch.half)
139
+ return t
140
+
141
+
142
+ def ocr(ckpt, img, out_img):
143
+ reader = ReaderLite()
144
+ overrides={"eval_cider":False, "beam":8, "max_len_b":128, "patch_image_size":480, "orig_patch_image_size":224, "no_repeat_ngram_size":0, "seed":7}
145
+ models, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
146
+ utils.split_paths(ckpt),
147
+ arg_overrides=overrides
148
+ )
149
+
150
+ # Move models to GPU
151
+ for model in models:
152
+ model.eval()
153
+ if use_fp16:
154
+ model.half()
155
+ if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
156
+ model.cuda()
157
+ model.prepare_for_inference_(cfg)
158
+
159
+ # Initialize generator
160
+ generator = task.build_generator(models, cfg.generation)
161
+
162
+ bos_item = torch.LongTensor([task.src_dict.bos()])
163
+ eos_item = torch.LongTensor([task.src_dict.eos()])
164
+ pad_idx = task.src_dict.pad()
165
+
166
+ orig_image = Image.open(img)
167
+ results = get_images(img, reader)
168
+ box_list, image_list = zip(*results)
169
+ draw_boxes(orig_image, box_list)
170
+ orig_image.save(out_img)
171
+
172
+ ocr_result = []
173
+ for box, image in zip(box_list, image_list):
174
+ image = Image.fromarray(image)
175
+ sample = construct_sample(task, image, cfg.task.patch_image_size)
176
+ sample = utils.move_to_cuda(sample) if use_cuda else sample
177
+ sample = utils.apply_to_sample(apply_half, sample) if use_fp16 else sample
178
+
179
+ with torch.no_grad():
180
+ result, scores = eval_step(task, generator, models, sample)
181
+ ocr_result.append(result[0]['ocr'].replace(' ', ''))
182
+
183
+ result = '\n'.join(ocr_result)
184
+ return result
185
+
186
+
187
+ title = "OFA-OCR"
188
+ description = "Gradio Demo for OFA-OCR. Upload your own image or click any one of the examples, and click " \
189
+ "\"Submit\" and then wait for the generated OCR result. "
190
+ article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \
191
+ "Repo</a></p> "
192
+ examples = [['a.jpg'], ['aurora.jpeg'], ['good_luck.png'], ['pokemons.jpg'], ['donuts.jpg']]
193
+ io = gr.Interface(fn=ocr, inputs=gr.inputs.Image(type='pil'), outputs=gr.outputs.Textbox(label="Caption"),
194
+ title=title, description=description, article=article, examples=examples,
195
+ allow_flagging=False, allow_screenshot=False)
196
+ io.launch(cache_examples=True)
197
+
checkpoints.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Checkpoints
2
+
3
+ We provide links for you to download our checkpoints, including pretrained and finetuned models on different tasks. If you would like to use OFA with Transformers, please download checkpoints at [https://huggingface.co/OFA-Sys](https://huggingface.co/OFA-Sys), and check the code in the branch `feature/add_transformers`.
4
+
5
+ ## Pretraining
6
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_huge.pt"> Pre-trained checkpoint (OFA-Huge) </a> (~930M parameters)
7
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_large.pt"> Pre-trained checkpoint (OFA-Large) </a> (~470M parameters)
8
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_base.pt"> Pre-trained checkpoint (OFA-Base) </a> (~180M parameters)
9
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_medium.pt"> Pre-trained checkpoint (OFA-Medium) </a> (~93M parameters)
10
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_tiny.pt"> Pre-trained checkpoint (OFA-Tiny) </a> (~33M parameters)
11
+
12
+ ## Finetuning (OFA-Huge)
13
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_huge_best.pt"> Finetuned checkpoint for Caption on COCO </a>
14
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/vqa_huge_best.pt"> Finetuned checkpoint for VQAv2 </a>
15
+
16
+ ## Finetuning (OFA-Large)
17
+
18
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_large_best_clean.pt"> Finetuned checkpoint for Caption on COCO </a>
19
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_stage1_best.pt"> Finetuned checkpoint for Caption on COCO During Stage1 Finetuning </a>
20
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_large_best.pt"> Finetuned checkpoint for RefCOCO </a>
21
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_large_best.pt"> Finetuned checkpoint for RefCOCO+ </a>
22
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_large_best.pt"> Finetuned checkpoint for RefCOCOg </a>
23
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/vqa_large_best.pt"> Finetuned checkpoint for VQAv2 </a>
24
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/snli_ve_large_best.pt"> Finetuned checkpoint for SNLI-VE </a>
25
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/image_gen_large_best.zip"> Finetuned checkpoint for Text-to-Image Generation on COCO && CLIP checkpoint && VQGAN checkpoint </a>
26
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/imagenet_1k_large_best.pt"> Finetuned checkpoint for ImageNet-1K </a>
27
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/gigaword_large_best.pt"> Finetuned checkpoint for Gigaword </a>
28
+
29
+
30
+ ## Finetuning (OFA-Base)
31
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_base_best.pt"> Finetuned base checkpoint for Caption on COCO </a>
32
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_base_best.pt"> Finetuned base checkpoint for RefCOCO </a>
33
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_base_best.pt"> Finetuned base checkpoint for RefCOCO+ </a>
34
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_base_best.pt"> Finetuned base checkpoint for RefCOCOg </a>
35
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/vqa_base_best.pt"> Finetuned base checkpoint for VQAv2 </a>
36
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/snli_ve_base_best.pt"> Finetuned base checkpoint for SNLI-VE </a>
37
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/image_gen_base_best.pt"> Finetuned base checkpoint for Text-to-Image Generation on COCO </a>
checkpoints_cn.md ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Checkpoints (OFA-CN)
2
+
3
+ We provide checkpoints of OFA-CN, which is the Chinese version of OFA. We provide Base-size and Large-size models, including pretrained and finetuned models on image captioning and referring expression comprehension. Note that we translated the texts in the RefCOCO(-/+/g) datasets and finetuned OFA-CN on them. We plan to release the related new datasets in the near future.
4
+ <br>
5
+
6
+ ## Checkpoints
7
+ Below we provide the links for downloading the Chinese OFA checkpoints.
8
+
9
+ ### Pretraining
10
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_cn_large.pt"> Pretrained checkpoint (OFA-CN-Large) </a> (~443M parameters)
11
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_cn_base.pt "> Pretrained checkpoint (OFA-CN-Base) </a> (~160M parameters)
12
+
13
+ ### Finetuning (OFA-Large)
14
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_cn_large.pt"> Finetuned checkpoint for MUGE Caption (Stage 1) </a>
15
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_cn_large.pt"> Finetuned checkpoint for RefCOCO-CN </a>
16
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_cn_large.pt"> Finetuned checkpoint for RefCOCO+-CN </a>
17
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_cn_large.pt"> Finetuned checkpoint for RefCOCOg-CN </a>
18
+
19
+ ### Finetuning (OFA-Base)
20
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_cn_base.pt"> Finetuned checkpoint for MUGE Caption (Stage 1) </a>
21
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_cn_base.pt"> Finetuned checkpoint for RefCOCO-CN </a>
22
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_cn_base.pt"> Finetuned checkpoint for RefCOCO+-CN </a>
23
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_cn_base.pt"> Finetuned checkpoint for RefCOCOg-CN </a>
24
+ <br>
25
+
26
+ ## Model Card
27
+ Below we provide the basic information of the base-size and large-size OFA-CN.
28
+
29
+ <table border="1" width="100%">
30
+ <tr align="center">
31
+ <th>Model</th><th>#Params</th><th>Backbone</th><th>Hidden Size</th><th>Intermediate Size</th><th>#Heads</th><th>#Enc. Layers</th><th>#Dec. Layers</th>
32
+ </tr>
33
+ <tr align="center">
34
+ <td>OFA<sub>Base</sub><td>160M</td><td>ResNet101</td><td>768</td></td><td>3072</td><td>12</td><td>6</td><td>6</td>
35
+ </tr>
36
+ <tr align="center">
37
+ <td>OFA<sub>Large</sub></td><td>443M</td><td>ResNet152</td><td>1024</td></td><td>4096</td><td>16</td><td>12</td><td>12</td>
38
+ </tr>
39
+ </tr>
40
+ </table>
41
+ <br>
42
+
43
+ ## Results
44
+ Below we provide the results of OFA-CN and the baselines for comparison.
45
+
46
+ ### [MUGE Caption]("https://tianchi.aliyun.com/muge")
47
+ <table border="1" width="100%">
48
+ <tr align="center">
49
+ <td>Model</td><td>BLEU@4</td><td>ROUGE-L</td><td>CIDEr-D</td>
50
+ </tr>
51
+ <tr align="center">
52
+ <td>Trm </td><td>7.33</td><td>51.51</td><td>11.00</td>
53
+ </tr>
54
+ <tr align="center">
55
+ <td>M6</td><td>16.19</td><td>55.06</td><td>30.75</td>
56
+ </tr>
57
+ <tr align="center">
58
+ <td>OFA<sub>Base</sub></td><td>26.23</td><td>58.95</td><td>50.70</td>
59
+ </tr>
60
+ <tr align="center">
61
+ <td>OFA<sub>Large</sub></td><td><b>27.32</b></td><td><b>59.20</b></td><td><b>53.51</b></td>
62
+ </tr>
63
+ </table>
64
+
65
+ ### RefCOCO-CN Series
66
+ <table border="1" width="100%">
67
+ <tr align="center">
68
+ <td>Model</td><td>RefCOCO(val/testA/testB)</td><td>RefCOCO+(val/testA/testB)</td><td>RefCOCOg(val/test-u)</td>
69
+ </tr>
70
+ <tr align="center">
71
+ <td>OFA<sub>Base</sub>(random-init)</td><td>30.13/35.07/25.03</td><td>17.89/20.90/15.83</td><td>20.30/20.45</td>
72
+ </tr>
73
+ <tr align="center">
74
+ <td>OFA<sub>Base</sub></td><td>82.18/86.07/<b>76.68</b></td><td>69.38/77.26/60.14</td><td><b>73.57/72.53</b></td>
75
+ </tr>
76
+ <tr align="center">
77
+ <td>OFA<sub>Large</sub></td><td><b>82.84/86.54</b>/76.50</td><td><b>71.30/78.56/61.85</b></td><td>71.96/71.30</td>
78
+ </tr>
79
+ </table>
80
+ <br>
81
+
82
+
colab.md ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ # Colab Notebooks
2
+
3
+ We provide Colab notebooks of different downstream tasks for you guys to enjoy OFA. See below.
4
+
5
+ * [Image Captioning in Huggingface Transformers](https://colab.research.google.com/drive/1Ho81RBV8jysZ7e0FhsSCk_v938QeDuy3?usp=sharing)
6
+ * [Generic Interface](https://colab.research.google.com/drive/1jogyZ-2rdHU3XxZOf3TBfhex1XHqX-1m?usp=sharing#scrollTo=s9Vni6YUZOpC) (using different instructions to perform various tasks with just one model.)
7
+ * [Image Captioning](https://colab.research.google.com/drive/1Q4eNhhhLcgOP4hHqwZwU1ijOlabgve1W?usp=sharing)
8
+ * [Referring Expression Comprehension](https://colab.research.google.com/drive/1AHQNRdaUpRTgr3XySHSlba8aXwBAjwPB?usp=sharing)
9
+ * [Open-Domain Visual Question Answering](https://colab.research.google.com/drive/1lsMsF-Vum3MVyXwSVF5E-Y23rHFvj_3y?usp=sharing)
criterions/__init__.py ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ from .scst_loss import ScstRewardCriterion
2
+ from .label_smoothed_cross_entropy import AdjustLabelSmoothedCrossEntropyCriterion
3
+ from .clip_scst_loss import ClipScstRewardCriterion
4
+ from .label_smoothed_encouraging_loss import AdjustLabelSmoothedEncouragingLossCriterion
criterions/clip_scst_loss.py ADDED
@@ -0,0 +1,277 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import math
7
+ from dataclasses import dataclass, field
8
+ from typing import Optional
9
+ from PIL import Image
10
+ from torchvision import transforms
11
+
12
+ import torch
13
+ import numpy as np
14
+ from fairseq import metrics
15
+ from fairseq.data import data_utils
16
+ from fairseq.criterions import FairseqCriterion, register_criterion
17
+ from fairseq.dataclass import FairseqDataclass
18
+ from fairseq import utils
19
+ from omegaconf import II
20
+
21
+ from models import clip
22
+
23
+
24
+ def custom_to_pil(x):
25
+ x = x.detach().cpu()
26
+ x = torch.clamp(x, -1., 1.)
27
+ x = (x + 1.) / 2.
28
+ x = x.permute(1, 2, 0).numpy()
29
+ x = (255 * x).astype(np.uint8)
30
+ x = Image.fromarray(x)
31
+ if not x.mode == "RGB":
32
+ x = x.convert("RGB")
33
+ return x
34
+
35
+
36
+ def scst_loss(lprobs, target, reward, ignore_index=None, reduce=True):
37
+ loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward.unsqueeze(-1)
38
+ if ignore_index is not None:
39
+ pad_mask = target.eq(ignore_index)
40
+ loss.masked_fill_(pad_mask, 0.0)
41
+ ntokens = (~pad_mask).sum()
42
+ else:
43
+ loss = loss.squeeze(-1)
44
+ ntokens = target.numel()
45
+ if reduce:
46
+ loss = loss.sum()
47
+ return loss, ntokens
48
+
49
+
50
+ @dataclass
51
+ class ClipScstRewardCriterionConfig(FairseqDataclass):
52
+ ignore_prefix_size: int = field(
53
+ default=0,
54
+ metadata={"help": "Ignore first N tokens"},
55
+ )
56
+ sentence_avg: bool = II("optimization.sentence_avg")
57
+ constraint_range: Optional[str] = field(
58
+ default=None,
59
+ metadata={"help": "constraint range"}
60
+ )
61
+
62
+
63
+ @register_criterion(
64
+ "clip_scst_reward_criterion", dataclass=ClipScstRewardCriterionConfig
65
+ )
66
+ class ClipScstRewardCriterion(FairseqCriterion):
67
+ CLIP_REWARD_WEIGHT = 2.5
68
+
69
+ def __init__(
70
+ self,
71
+ task,
72
+ sentence_avg,
73
+ ignore_prefix_size=0,
74
+ constraint_range=None
75
+ ):
76
+ super().__init__(task)
77
+ self.sentence_avg = sentence_avg
78
+ self.ignore_prefix_size = ignore_prefix_size
79
+
80
+ self.constraint_start = None
81
+ self.constraint_end = None
82
+ if constraint_range is not None:
83
+ constraint_start, constraint_end = constraint_range.split(',')
84
+ self.constraint_start = int(constraint_start)
85
+ self.constraint_end = int(constraint_end)
86
+
87
+ def forward(self, model, sample, update_num=0, reduce=True):
88
+ """Compute the loss for the given sample.
89
+
90
+ Returns a tuple with three elements:
91
+ 1) the loss
92
+ 2) the sample size, which is used as the denominator for the gradient
93
+ 3) logging outputs to display while training
94
+ """
95
+ loss, score, ntokens, nsentences = self.compute_loss(model, sample, reduce=reduce)
96
+
97
+ sample_size = (
98
+ nsentences if self.sentence_avg else ntokens
99
+ )
100
+ logging_output = {
101
+ "loss": loss.data,
102
+ "score": score,
103
+ "ntokens": ntokens,
104
+ "nsentences": nsentences,
105
+ "sample_size": sample_size,
106
+ }
107
+ return loss, sample_size, logging_output
108
+
109
+ def _calculate_clip_scores(self, gen_res, gt_text, device):
110
+ '''
111
+ gen_res: generated images, list of Image
112
+ gt_text: input captions.
113
+ device: device for clip model
114
+ '''
115
+ batch_size = len(gt_text)
116
+ gen_res_size = len(gen_res)
117
+ img_per_seq = gen_res_size // batch_size
118
+
119
+ hyp_images = torch.stack(
120
+ [self.task.clip_preprocess(gen_image) for gen_image in gen_res], dim=0
121
+ ).to(device)
122
+
123
+ clip_input = clip.tokenize([text for text in gt_text]).to(device)
124
+ with torch.no_grad():
125
+ image_features = self.task.clip_model.encode_image(hyp_images)
126
+ text_features = self.task.clip_model.encode_text(clip_input)
127
+ image_features /= image_features.norm(dim=-1, keepdim=True)
128
+ text_features /= text_features.norm(dim=-1, keepdim=True)
129
+ image_features = image_features.view(batch_size, img_per_seq, -1)
130
+ text_features = text_features.view(batch_size, 1, -1)
131
+ ti_similarity = image_features @ text_features.transpose(1, 2)
132
+ ti_similarity = ti_similarity.view(-1)
133
+
134
+ scores = self.CLIP_REWARD_WEIGHT * ti_similarity
135
+ return scores
136
+
137
+ def get_generator_out(self, model, sample):
138
+ model.eval()
139
+ with torch.no_grad():
140
+ self.task.scst_generator.model.eval()
141
+ gen_out = self.task.scst_generator.generate([model], sample)
142
+
143
+ gen_target = []
144
+ gen_res = []
145
+ gt_text = []
146
+ for i in range(len(gen_out)):
147
+ with torch.no_grad():
148
+ tokens = torch.stack([item['tokens'][:-1] for item in gen_out[i]], dim=0)
149
+ tokens += -len(self.task.src_dict) + self.task.cfg.code_dict_size + self.task.cfg.num_bins
150
+ images = self.task.image_tokenizer.decode_code(
151
+ tokens.view(-1, self.task.cfg.code_image_size // 8, self.task.cfg.code_image_size // 8)
152
+ )
153
+ images = [custom_to_pil(image) for image in images]
154
+
155
+ gen_target += [item['tokens'] for item in gen_out[i]]
156
+ gen_res += images
157
+ gt_text.append(
158
+ self.task.bpe.decode(
159
+ self.task.tgt_dict.string(
160
+ utils.strip_pad(sample['net_input']['src_tokens'][i], self.padding_idx).cpu().int()
161
+ )
162
+ )[38:] # remove task instruction.
163
+ )
164
+
165
+ return gen_target, gen_res, gt_text
166
+
167
+ def get_reward_and_scores(self, gen_res, gt_text, device):
168
+ batch_size = len(gt_text)
169
+ gen_res_size = len(gen_res)
170
+ img_per_sample = gen_res_size // batch_size
171
+
172
+ scores = self._calculate_clip_scores(gen_res, gt_text, device)
173
+ sc_ = scores.reshape(batch_size, img_per_sample)
174
+ baseline = (sc_.sum(1, keepdim=True) - sc_) / (sc_.shape[1] - 1)
175
+ # sample - baseline
176
+ reward = scores.reshape(batch_size, img_per_sample)
177
+ reward = reward - baseline
178
+ reward = reward.view(-1)
179
+
180
+ return reward, scores
181
+
182
+ def get_net_output(self, model, sample, gen_target):
183
+ def merge(sample_list, eos=self.task.tgt_dict.eos(), move_eos_to_beginning=False):
184
+ return data_utils.collate_tokens(
185
+ sample_list,
186
+ pad_idx=self.padding_idx,
187
+ eos_idx=eos,
188
+ left_pad=False,
189
+ move_eos_to_beginning=move_eos_to_beginning,
190
+ )
191
+
192
+ batch_size = len(sample["target"])
193
+ gen_target_size = len(gen_target)
194
+ img_per_sample = gen_target_size // batch_size
195
+
196
+ model.train()
197
+ sample_src_tokens = torch.repeat_interleave(
198
+ sample['net_input']['src_tokens'], img_per_sample, dim=0
199
+ )
200
+ sample_src_lengths = torch.repeat_interleave(
201
+ sample['net_input']['src_lengths'], img_per_sample, dim=0
202
+ )
203
+ sample_code_masks = torch.repeat_interleave(
204
+ sample['net_input']['code_masks'], img_per_sample, dim=0
205
+ )
206
+ gen_prev_output_tokens = torch.as_tensor(
207
+ merge(gen_target, eos=self.task.tgt_dict.bos(), move_eos_to_beginning=True),
208
+ device=sample["target"].device, dtype=torch.int64
209
+ )
210
+ gen_target_tokens = torch.as_tensor(
211
+ merge(gen_target), device=sample["target"].device, dtype=torch.int64
212
+ )
213
+ net_output = model(
214
+ src_tokens=sample_src_tokens, src_lengths=sample_src_lengths,
215
+ code_masks=sample_code_masks, prev_output_tokens=gen_prev_output_tokens
216
+ )
217
+
218
+ return net_output, gen_target_tokens
219
+
220
+ def get_lprobs_and_target(self, model, net_output, gen_target):
221
+ if self.constraint_start is not None and self.constraint_end is not None:
222
+ net_output[0][:, :, 4:self.constraint_start] = -math.inf
223
+ net_output[0][:, :, self.constraint_end:] = -math.inf
224
+ lprobs = model.get_normalized_probs(net_output, log_probs=True)
225
+ if self.ignore_prefix_size > 0:
226
+ if getattr(lprobs, "batch_first", False):
227
+ lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
228
+ gen_target = gen_target[:, self.ignore_prefix_size :].contiguous()
229
+ else:
230
+ lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
231
+ gen_target = gen_target[self.ignore_prefix_size :, :].contiguous()
232
+ return lprobs, gen_target
233
+
234
+ def compute_loss(self, model, sample, reduce=True):
235
+ gen_target, gen_res, gt_text = self.get_generator_out(model, sample)
236
+ reward, scores = self.get_reward_and_scores(gen_res, gt_text, device=sample["target"].device)
237
+ net_output, gen_target_tokens = self.get_net_output(model, sample, gen_target)
238
+ gen_lprobs, gen_target_tokens = self.get_lprobs_and_target(model, net_output, gen_target_tokens)
239
+ loss, ntokens = scst_loss(gen_lprobs, gen_target_tokens, reward, ignore_index=self.padding_idx, reduce=reduce)
240
+ nsentences = gen_target_tokens.size(0)
241
+
242
+ return loss, scores.sum(), ntokens, nsentences
243
+
244
+ @classmethod
245
+ def reduce_metrics(cls, logging_outputs) -> None:
246
+ """Aggregate logging outputs from data parallel training."""
247
+ loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
248
+ score_sum = sum(log.get("score", 0) for log in logging_outputs)
249
+ ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
250
+ nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
251
+ sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
252
+
253
+ metrics.log_scalar(
254
+ "loss", loss_sum / sample_size, sample_size, round=3
255
+ )
256
+ metrics.log_scalar(
257
+ "score", score_sum / nsentences, nsentences, round=3
258
+ )
259
+
260
+ metrics.log_scalar(
261
+ "ntokens", ntokens, 1, round=3
262
+ )
263
+ metrics.log_scalar(
264
+ "nsentences", nsentences, 1, round=3
265
+ )
266
+ metrics.log_scalar(
267
+ "sample_size", sample_size, 1, round=3
268
+ )
269
+
270
+ @staticmethod
271
+ def logging_outputs_can_be_summed() -> bool:
272
+ """
273
+ Whether the logging outputs returned by `forward` can be summed
274
+ across workers prior to calling `reduce_metrics`. Setting this
275
+ to True will improves distributed training speed.
276
+ """
277
+ return True
criterions/label_smoothed_cross_entropy.py ADDED
@@ -0,0 +1,343 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import math
7
+ from dataclasses import dataclass, field
8
+ from typing import Optional
9
+
10
+ import torch
11
+ import torch.nn.functional as F
12
+ import numpy as np
13
+ from fairseq import metrics, utils
14
+ from fairseq.criterions import FairseqCriterion, register_criterion
15
+ from fairseq.dataclass import FairseqDataclass
16
+ from omegaconf import II
17
+
18
+
19
+ @dataclass
20
+ class AdjustLabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
21
+ label_smoothing: float = field(
22
+ default=0.0,
23
+ metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
24
+ )
25
+ report_accuracy: bool = field(
26
+ default=False,
27
+ metadata={"help": "report accuracy metric"},
28
+ )
29
+ ignore_prefix_size: int = field(
30
+ default=0,
31
+ metadata={"help": "Ignore first N tokens"},
32
+ )
33
+ ignore_eos: bool = field(
34
+ default=False,
35
+ metadata={"help": "Ignore eos token"},
36
+ )
37
+ sentence_avg: bool = II("optimization.sentence_avg")
38
+ drop_worst_ratio: float = field(
39
+ default=0.0,
40
+ metadata={"help": "ratio for discarding bad samples"},
41
+ )
42
+ drop_worst_after: int = field(
43
+ default=0,
44
+ metadata={"help": "steps for discarding bad samples"},
45
+ )
46
+ use_rdrop: bool = field(
47
+ default=False, metadata={"help": "use R-Drop"}
48
+ )
49
+ reg_alpha: float = field(
50
+ default=1.0, metadata={"help": "weight for R-Drop"}
51
+ )
52
+ sample_patch_num: int = field(
53
+ default=196, metadata={"help": "sample patches for v1"}
54
+ )
55
+ constraint_range: Optional[str] = field(
56
+ default=None,
57
+ metadata={"help": "constraint range"}
58
+ )
59
+
60
+
61
+ def construct_rdrop_sample(x):
62
+ if isinstance(x, dict):
63
+ for key in x:
64
+ x[key] = construct_rdrop_sample(x[key])
65
+ return x
66
+ elif isinstance(x, torch.Tensor):
67
+ return x.repeat(2, *([1] * (x.dim()-1)))
68
+ elif isinstance(x, int):
69
+ return x * 2
70
+ elif isinstance(x, np.ndarray):
71
+ return x.repeat(2)
72
+ else:
73
+ raise NotImplementedError
74
+
75
+
76
+ def kl_loss(p, q):
77
+ p_loss = F.kl_div(p, torch.exp(q), reduction='sum')
78
+ q_loss = F.kl_div(q, torch.exp(p), reduction='sum')
79
+ loss = (p_loss + q_loss) / 2
80
+ return loss
81
+
82
+
83
+ def label_smoothed_nll_loss(
84
+ lprobs, target, epsilon, update_num, reduce=True,
85
+ drop_worst_ratio=0.0, drop_worst_after=0, use_rdrop=False, reg_alpha=1.0,
86
+ constraint_masks=None, constraint_start=None, constraint_end=None
87
+ ):
88
+ if target.dim() == lprobs.dim() - 1:
89
+ target = target.unsqueeze(-1)
90
+ nll_loss = -lprobs.gather(dim=-1, index=target).squeeze(-1)
91
+ if constraint_masks is not None:
92
+ smooth_loss = -lprobs.masked_fill(~constraint_masks, 0).sum(dim=-1, keepdim=True).squeeze(-1)
93
+ eps_i = epsilon / (constraint_masks.sum(1) - 1 + 1e-6)
94
+ elif constraint_start is not None and constraint_end is not None:
95
+ constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
96
+ smooth_loss = -lprobs[:, constraint_range].sum(dim=-1, keepdim=True).squeeze(-1)
97
+ eps_i = epsilon / (len(constraint_range) - 1 + 1e-6)
98
+ else:
99
+ smooth_loss = -lprobs.sum(dim=-1, keepdim=True).squeeze(-1)
100
+ eps_i = epsilon / (lprobs.size(-1) - 1)
101
+ loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
102
+ if drop_worst_ratio > 0 and update_num > drop_worst_after:
103
+ if use_rdrop:
104
+ true_batch_size = loss.size(0) // 2
105
+ _, indices = torch.topk(loss[:true_batch_size], k=int(true_batch_size * (1 - drop_worst_ratio)), largest=False)
106
+ loss = torch.cat([loss[indices], loss[indices+true_batch_size]])
107
+ nll_loss = torch.cat([nll_loss[indices], nll_loss[indices+true_batch_size]])
108
+ lprobs = torch.cat([lprobs[indices], lprobs[indices+true_batch_size]])
109
+ else:
110
+ loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_worst_ratio)), largest=False)
111
+ nll_loss = nll_loss[indices]
112
+ lprobs = lprobs[indices]
113
+
114
+ ntokens = loss.numel()
115
+ nll_loss = nll_loss.sum()
116
+ loss = loss.sum()
117
+ if use_rdrop:
118
+ true_batch_size = lprobs.size(0) // 2
119
+ p = lprobs[:true_batch_size]
120
+ q = lprobs[true_batch_size:]
121
+ if constraint_start is not None and constraint_end is not None:
122
+ constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
123
+ p = p[:, constraint_range]
124
+ q = q[:, constraint_range]
125
+ loss += kl_loss(p, q) * reg_alpha
126
+
127
+ return loss, nll_loss, ntokens
128
+
129
+
130
+ @register_criterion(
131
+ "adjust_label_smoothed_cross_entropy", dataclass=AdjustLabelSmoothedCrossEntropyCriterionConfig
132
+ )
133
+ class AdjustLabelSmoothedCrossEntropyCriterion(FairseqCriterion):
134
+ def __init__(
135
+ self,
136
+ task,
137
+ sentence_avg,
138
+ label_smoothing,
139
+ ignore_prefix_size=0,
140
+ ignore_eos=False,
141
+ report_accuracy=False,
142
+ drop_worst_ratio=0,
143
+ drop_worst_after=0,
144
+ use_rdrop=False,
145
+ reg_alpha=1.0,
146
+ sample_patch_num=196,
147
+ constraint_range=None
148
+ ):
149
+ super().__init__(task)
150
+ self.sentence_avg = sentence_avg
151
+ self.eps = label_smoothing
152
+ self.ignore_prefix_size = ignore_prefix_size
153
+ self.ignore_eos = ignore_eos
154
+ self.report_accuracy = report_accuracy
155
+ self.drop_worst_ratio = drop_worst_ratio
156
+ self.drop_worst_after = drop_worst_after
157
+ self.use_rdrop = use_rdrop
158
+ self.reg_alpha = reg_alpha
159
+ self.sample_patch_num = sample_patch_num
160
+
161
+ self.constraint_start = None
162
+ self.constraint_end = None
163
+ if constraint_range is not None:
164
+ constraint_start, constraint_end = constraint_range.split(',')
165
+ self.constraint_start = int(constraint_start)
166
+ self.constraint_end = int(constraint_end)
167
+
168
+ def forward(self, model, sample, update_num=0, reduce=True):
169
+ """Compute the loss for the given sample.
170
+
171
+ Returns a tuple with three elements:
172
+ 1) the loss
173
+ 2) the sample size, which is used as the denominator for the gradient
174
+ 3) logging outputs to display while training
175
+ """
176
+ if isinstance(sample, list):
177
+ if self.sample_patch_num > 0:
178
+ sample[0]['net_input']['sample_patch_num'] = self.sample_patch_num
179
+ loss_v1, sample_size_v1, logging_output_v1 = self.forward(model, sample[0], update_num, reduce)
180
+ loss_v2, sample_size_v2, logging_output_v2 = self.forward(model, sample[1], update_num, reduce)
181
+ loss = loss_v1 / sample_size_v1 + loss_v2 / sample_size_v2
182
+ sample_size = 1
183
+ logging_output = {
184
+ "loss": loss.data,
185
+ "loss_v1": loss_v1.data,
186
+ "loss_v2": loss_v2.data,
187
+ "nll_loss": logging_output_v1["nll_loss"].data / sample_size_v1 + logging_output_v2["nll_loss"].data / sample_size_v2,
188
+ "ntokens": logging_output_v1["ntokens"] + logging_output_v2["ntokens"],
189
+ "nsentences": logging_output_v1["nsentences"] + logging_output_v2["nsentences"],
190
+ "sample_size": 1,
191
+ "sample_size_v1": sample_size_v1,
192
+ "sample_size_v2": sample_size_v2,
193
+ }
194
+ return loss, sample_size, logging_output
195
+
196
+ if self.use_rdrop:
197
+ construct_rdrop_sample(sample)
198
+
199
+ net_output = model(**sample["net_input"])
200
+ loss, nll_loss, ntokens = self.compute_loss(model, net_output, sample, update_num, reduce=reduce)
201
+ sample_size = (
202
+ sample["target"].size(0) if self.sentence_avg else ntokens
203
+ )
204
+ logging_output = {
205
+ "loss": loss.data,
206
+ "nll_loss": nll_loss.data,
207
+ "ntokens": sample["ntokens"],
208
+ "nsentences": sample["nsentences"],
209
+ "sample_size": sample_size,
210
+ }
211
+ if self.report_accuracy:
212
+ n_correct, total = self.compute_accuracy(model, net_output, sample)
213
+ logging_output["n_correct"] = utils.item(n_correct.data)
214
+ logging_output["total"] = utils.item(total.data)
215
+ return loss, sample_size, logging_output
216
+
217
+ def get_lprobs_and_target(self, model, net_output, sample):
218
+ conf = sample['conf'][:, None, None] if 'conf' in sample and sample['conf'] is not None else 1
219
+ constraint_masks = None
220
+ if "constraint_masks" in sample and sample["constraint_masks"] is not None:
221
+ constraint_masks = sample["constraint_masks"]
222
+ net_output[0].masked_fill_(~constraint_masks, -math.inf)
223
+ if self.constraint_start is not None and self.constraint_end is not None:
224
+ net_output[0][:, :, 4:self.constraint_start] = -math.inf
225
+ net_output[0][:, :, self.constraint_end:] = -math.inf
226
+ lprobs = model.get_normalized_probs(net_output, log_probs=True) * conf
227
+ target = model.get_targets(sample, net_output)
228
+ if self.ignore_prefix_size > 0:
229
+ lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
230
+ target = target[:, self.ignore_prefix_size :].contiguous()
231
+ if constraint_masks is not None:
232
+ constraint_masks = constraint_masks[:, self.ignore_prefix_size :, :].contiguous()
233
+ if self.ignore_eos:
234
+ bsz, seq_len, embed_dim = lprobs.size()
235
+ eos_indices = target.eq(self.task.tgt_dict.eos())
236
+ lprobs = lprobs[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
237
+ target = target[~eos_indices].reshape(bsz, seq_len-1)
238
+ if constraint_masks is not None:
239
+ constraint_masks = constraint_masks[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
240
+ if constraint_masks is not None:
241
+ constraint_masks = constraint_masks.view(-1, constraint_masks.size(-1))
242
+ return lprobs.view(-1, lprobs.size(-1)), target.view(-1), constraint_masks
243
+
244
+ def compute_loss(self, model, net_output, sample, update_num, reduce=True):
245
+ lprobs, target, constraint_masks = self.get_lprobs_and_target(model, net_output, sample)
246
+ if constraint_masks is not None:
247
+ constraint_masks = constraint_masks[target != self.padding_idx]
248
+ lprobs = lprobs[target != self.padding_idx]
249
+ target = target[target != self.padding_idx]
250
+ loss, nll_loss, ntokens = label_smoothed_nll_loss(
251
+ lprobs,
252
+ target,
253
+ self.eps,
254
+ update_num,
255
+ reduce=reduce,
256
+ drop_worst_ratio=self.drop_worst_ratio,
257
+ drop_worst_after=self.drop_worst_after,
258
+ use_rdrop=self.use_rdrop,
259
+ reg_alpha=self.reg_alpha,
260
+ constraint_masks=constraint_masks,
261
+ constraint_start=self.constraint_start,
262
+ constraint_end=self.constraint_end
263
+ )
264
+ return loss, nll_loss, ntokens
265
+
266
+ def compute_accuracy(self, model, net_output, sample):
267
+ lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
268
+ mask = target.ne(self.padding_idx)
269
+ n_correct = torch.sum(
270
+ lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
271
+ )
272
+ total = torch.sum(mask)
273
+ return n_correct, total
274
+
275
+ @classmethod
276
+ def reduce_metrics(cls, logging_outputs) -> None:
277
+ """Aggregate logging outputs from data parallel training."""
278
+ loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
279
+ loss_sum_v1 = sum(log.get("loss_v1", 0) for log in logging_outputs)
280
+ loss_sum_v2 = sum(log.get("loss_v2", 0) for log in logging_outputs)
281
+ nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
282
+ ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
283
+ nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
284
+ sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
285
+ sample_size_v1 = sum(log.get("sample_size_v1", 0) for log in logging_outputs)
286
+ sample_size_v2 = sum(log.get("sample_size_v2", 0) for log in logging_outputs)
287
+
288
+ metrics.log_scalar(
289
+ "loss", loss_sum / sample_size, sample_size, round=3
290
+ )
291
+ metrics.log_scalar(
292
+ "loss_v1", loss_sum_v1 / max(sample_size_v1, 1), max(sample_size_v1, 1), round=3
293
+ )
294
+ metrics.log_scalar(
295
+ "loss_v2", loss_sum_v2 / max(sample_size_v2, 1), max(sample_size_v2, 1), round=3
296
+ )
297
+ metrics.log_scalar(
298
+ "nll_loss", nll_loss_sum / sample_size, ntokens, round=3
299
+ )
300
+ metrics.log_derived(
301
+ "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
302
+ )
303
+
304
+ metrics.log_scalar(
305
+ "ntokens", ntokens, 1, round=3
306
+ )
307
+ metrics.log_scalar(
308
+ "nsentences", nsentences, 1, round=3
309
+ )
310
+ metrics.log_scalar(
311
+ "sample_size", sample_size, 1, round=3
312
+ )
313
+ metrics.log_scalar(
314
+ "sample_size_v1", sample_size_v1, 1, round=3
315
+ )
316
+ metrics.log_scalar(
317
+ "sample_size_v2", sample_size_v2, 1, round=3
318
+ )
319
+
320
+ total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
321
+ if total > 0:
322
+ metrics.log_scalar("total", total)
323
+ n_correct = utils.item(
324
+ sum(log.get("n_correct", 0) for log in logging_outputs)
325
+ )
326
+ metrics.log_scalar("n_correct", n_correct)
327
+ metrics.log_derived(
328
+ "accuracy",
329
+ lambda meters: round(
330
+ meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
331
+ )
332
+ if meters["total"].sum > 0
333
+ else float("nan"),
334
+ )
335
+
336
+ @staticmethod
337
+ def logging_outputs_can_be_summed() -> bool:
338
+ """
339
+ Whether the logging outputs returned by `forward` can be summed
340
+ across workers prior to calling `reduce_metrics`. Setting this
341
+ to True will improves distributed training speed.
342
+ """
343
+ return True
criterions/label_smoothed_encouraging_loss.py ADDED
@@ -0,0 +1,395 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Facebook, Inc. and its affiliates.
2
+ #
3
+ # This source code is licensed under the MIT license found in the
4
+ # LICENSE file in the root directory of this source tree.
5
+
6
+ import math
7
+ from dataclasses import dataclass, field
8
+ from typing import Optional
9
+
10
+ import torch
11
+ import torch.nn.functional as F
12
+ import numpy as np
13
+ from fairseq import metrics, utils
14
+ from fairseq.criterions import FairseqCriterion, register_criterion
15
+ from fairseq.dataclass import FairseqDataclass
16
+ from omegaconf import II
17
+
18
+
19
+ @dataclass
20
+ class AdjustLabelSmoothedEncouragingLossConfig(FairseqDataclass):
21
+ label_smoothing: float = field(
22
+ default=0.0,
23
+ metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
24
+ )
25
+ report_accuracy: bool = field(
26
+ default=False,
27
+ metadata={"help": "report accuracy metric"},
28
+ )
29
+ ignore_prefix_size: int = field(
30
+ default=0,
31
+ metadata={"help": "Ignore first N tokens"},
32
+ )
33
+ ignore_eos: bool = field(
34
+ default=False,
35
+ metadata={"help": "Ignore eos token"},
36
+ )
37
+ sentence_avg: bool = II("optimization.sentence_avg")
38
+ drop_worst_ratio: float = field(
39
+ default=0.0,
40
+ metadata={"help": "ratio for discarding bad samples"},
41
+ )
42
+ drop_worst_after: int = field(
43
+ default=0,
44
+ metadata={"help": "steps for discarding bad samples"},
45
+ )
46
+ use_rdrop: bool = field(
47
+ default=False, metadata={"help": "use R-Drop"}
48
+ )
49
+ reg_alpha: float = field(
50
+ default=1.0, metadata={"help": "weight for R-Drop"}
51
+ )
52
+ sample_patch_num: int = field(
53
+ default=196, metadata={"help": "sample patchs for v1"}
54
+ )
55
+ constraint_range: Optional[str] = field(
56
+ default=None,
57
+ metadata={"help": "constraint range"}
58
+ )
59
+ log_end: float = field(
60
+ default=0.75,
61
+ metadata={"help": "higher log_end is for cases with higher performance,"
62
+ " we recommend 0.75 or 0.5 as your first try."}
63
+ )
64
+ drop_best_ratio: float = field(
65
+ default=0.0,
66
+ metadata={"help": "ratio for discarding best samples"},
67
+ )
68
+ drop_best_after: int = field(
69
+ default=0,
70
+ metadata={"help": "steps for discarding best samples"},
71
+ )
72
+
73
+
74
+
75
+ def construct_rdrop_sample(x):
76
+ if isinstance(x, dict):
77
+ for key in x:
78
+ x[key] = construct_rdrop_sample(x[key])
79
+ return x
80
+ elif isinstance(x, torch.Tensor):
81
+ return x.repeat(2, *([1] * (x.dim()-1)))
82
+ elif isinstance(x, int):
83
+ return x * 2
84
+ elif isinstance(x, np.ndarray):
85
+ return x.repeat(2)
86
+ else:
87
+ raise NotImplementedError
88
+
89
+
90
+ def kl_loss(p, q):
91
+ p_loss = F.kl_div(p, torch.exp(q), reduction='sum')
92
+ q_loss = F.kl_div(q, torch.exp(p), reduction='sum')
93
+ loss = (p_loss + q_loss) / 2
94
+ return loss
95
+
96
+
97
+ def label_smoothed_nll_loss(
98
+ lprobs, target, epsilon, update_num, reduce=True,
99
+ drop_worst_ratio=0.0, drop_worst_after=0, use_rdrop=False, reg_alpha=1.0,
100
+ constraint_masks=None, constraint_start=None, constraint_end=None, drop_best_ratio=0.0,
101
+ drop_best_after=0,
102
+ ):
103
+ if target.dim() == lprobs.dim() - 1:
104
+ target = target.unsqueeze(-1)
105
+ nll_loss = -lprobs.gather(dim=-1, index=target).squeeze(-1)
106
+ if constraint_masks is not None:
107
+ smooth_loss = -lprobs.masked_fill(~constraint_masks, 0).sum(dim=-1, keepdim=True).squeeze(-1)
108
+ eps_i = epsilon / (constraint_masks.sum(1) - 1 + 1e-6)
109
+ elif constraint_start is not None and constraint_end is not None:
110
+ constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
111
+ smooth_loss = -lprobs[:, constraint_range].sum(dim=-1, keepdim=True).squeeze(-1)
112
+ eps_i = epsilon / (len(constraint_range) - 1 + 1e-6)
113
+ else:
114
+ smooth_loss = -lprobs.sum(dim=-1, keepdim=True).squeeze(-1)
115
+ eps_i = epsilon / (lprobs.size(-1) - 1)
116
+ loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
117
+ if drop_worst_ratio > 0 and update_num > drop_worst_after:
118
+ if use_rdrop:
119
+ true_batch_size = loss.size(0) // 2
120
+ _, indices = torch.topk(loss[:true_batch_size], k=int(true_batch_size * (1 - drop_worst_ratio)), largest=False)
121
+ loss = torch.cat([loss[indices], loss[indices+true_batch_size]])
122
+ nll_loss = torch.cat([nll_loss[indices], nll_loss[indices+true_batch_size]])
123
+ lprobs = torch.cat([lprobs[indices], lprobs[indices+true_batch_size]])
124
+ else:
125
+ loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_worst_ratio)), largest=False)
126
+ nll_loss = nll_loss[indices]
127
+ lprobs = lprobs[indices]
128
+ target = target[indices]
129
+ if update_num > drop_best_after:
130
+ loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_best_ratio)), largest=True)
131
+ nll_loss = nll_loss[indices]
132
+ lprobs = lprobs[indices]
133
+ target = target[indices]
134
+
135
+ ntokens = loss.numel()
136
+ nll_loss = nll_loss.sum()
137
+ loss = loss.sum()
138
+ if use_rdrop:
139
+ true_batch_size = lprobs.size(0) // 2
140
+ p = lprobs[:true_batch_size]
141
+ q = lprobs[true_batch_size:]
142
+ if constraint_start is not None and constraint_end is not None:
143
+ constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
144
+ p = p[:, constraint_range]
145
+ q = q[:, constraint_range]
146
+ loss += kl_loss(p, q) * reg_alpha
147
+
148
+ return loss, nll_loss, ntokens,lprobs,target
149
+
150
+
151
+ @register_criterion(
152
+ "adjust_label_smoothed_encouraging_loss", dataclass=AdjustLabelSmoothedEncouragingLossConfig
153
+ )
154
+ class AdjustLabelSmoothedEncouragingLossCriterion(FairseqCriterion):
155
+ def __init__(
156
+ self,
157
+ task,
158
+ sentence_avg,
159
+ label_smoothing,
160
+ ignore_prefix_size=0,
161
+ ignore_eos=False,
162
+ report_accuracy=False,
163
+ drop_worst_ratio=0,
164
+ drop_worst_after=0,
165
+ use_rdrop=False,
166
+ reg_alpha=1.0,
167
+ sample_patch_num=196,
168
+ constraint_range=None,
169
+ log_end=0.75,
170
+ drop_best_ratio=0.0,
171
+ drop_best_after=0,
172
+ ):
173
+ super().__init__(task)
174
+ self.sentence_avg = sentence_avg
175
+ self.eps = label_smoothing
176
+ self.ignore_prefix_size = ignore_prefix_size
177
+ self.ignore_eos = ignore_eos
178
+ self.report_accuracy = report_accuracy
179
+ self.drop_worst_ratio = drop_worst_ratio
180
+ self.drop_worst_after = drop_worst_after
181
+ self.use_rdrop = use_rdrop
182
+ self.reg_alpha = reg_alpha
183
+ self.sample_patch_num = sample_patch_num
184
+
185
+ self.constraint_start = None
186
+ self.constraint_end = None
187
+ if constraint_range is not None:
188
+ constraint_start, constraint_end = constraint_range.split(',')
189
+ self.constraint_start = int(constraint_start)
190
+ self.constraint_end = int(constraint_end)
191
+ self.log_end = log_end
192
+ self.drop_best_ratio = drop_best_ratio
193
+ self.drop_best_after = drop_best_after
194
+ print('el, self.log_end=', self.log_end)
195
+ # @staticmethod
196
+ # def add_args(parser):
197
+ # """Add criterion-specific arguments to the parser."""
198
+ # # fmt: off
199
+ # parser.add_argument('--log_end', type=float, default=1.0)
200
+
201
+ def forward(self, model, sample, update_num=0, reduce=True):
202
+ """Compute the loss for the given sample.
203
+
204
+ Returns a tuple with three elements:
205
+ 1) the loss
206
+ 2) the sample size, which is used as the denominator for the gradient
207
+ 3) logging outputs to display while training
208
+ """
209
+ if isinstance(sample, list):
210
+ if self.sample_patch_num > 0:
211
+ sample[0]['net_input']['sample_patch_num'] = self.sample_patch_num
212
+ loss_v1, sample_size_v1, logging_output_v1 = self.forward(model, sample[0], update_num, reduce)
213
+ loss_v2, sample_size_v2, logging_output_v2 = self.forward(model, sample[1], update_num, reduce)
214
+ loss = loss_v1 / sample_size_v1 + loss_v2 / sample_size_v2
215
+ sample_size = 1
216
+ logging_output = {
217
+ "loss": loss.data,
218
+ "loss_v1": loss_v1.data,
219
+ "loss_v2": loss_v2.data,
220
+ "nll_loss": logging_output_v1["nll_loss"].data / sample_size_v1 + logging_output_v2["nll_loss"].data / sample_size_v2,
221
+ "ntokens": logging_output_v1["ntokens"] + logging_output_v2["ntokens"],
222
+ "nsentences": logging_output_v1["nsentences"] + logging_output_v2["nsentences"],
223
+ "sample_size": 1,
224
+ "sample_size_v1": sample_size_v1,
225
+ "sample_size_v2": sample_size_v2,
226
+ }
227
+ return loss, sample_size, logging_output
228
+
229
+ if self.use_rdrop:
230
+ construct_rdrop_sample(sample)
231
+
232
+ net_output = model(**sample["net_input"])
233
+ loss, nll_loss, ntokens = self.compute_loss(model, net_output, sample, update_num, reduce=reduce)
234
+ sample_size = (
235
+ sample["target"].size(0) if self.sentence_avg else ntokens
236
+ )
237
+ logging_output = {
238
+ "loss": loss.data,
239
+ "nll_loss": nll_loss.data,
240
+ "ntokens": sample["ntokens"],
241
+ "nsentences": sample["nsentences"],
242
+ "sample_size": sample_size,
243
+ }
244
+ if self.report_accuracy:
245
+ n_correct, total = self.compute_accuracy(model, net_output, sample)
246
+ logging_output["n_correct"] = utils.item(n_correct.data)
247
+ logging_output["total"] = utils.item(total.data)
248
+ return loss, sample_size, logging_output
249
+
250
+ def get_lprobs_and_target(self, model, net_output, sample):
251
+ conf = sample['conf'][:, None, None] if 'conf' in sample and sample['conf'] is not None else 1
252
+ constraint_masks = None
253
+ if "constraint_masks" in sample and sample["constraint_masks"] is not None:
254
+ constraint_masks = sample["constraint_masks"]
255
+ net_output[0].masked_fill_(~constraint_masks, -math.inf)
256
+ if self.constraint_start is not None and self.constraint_end is not None:
257
+ net_output[0][:, :, 4:self.constraint_start] = -math.inf
258
+ net_output[0][:, :, self.constraint_end:] = -math.inf
259
+ lprobs = model.get_normalized_probs(net_output, log_probs=True) * conf
260
+ target = model.get_targets(sample, net_output)
261
+ if self.ignore_prefix_size > 0:
262
+ lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
263
+ target = target[:, self.ignore_prefix_size :].contiguous()
264
+ if constraint_masks is not None:
265
+ constraint_masks = constraint_masks[:, self.ignore_prefix_size :, :].contiguous()
266
+ if self.ignore_eos:
267
+ bsz, seq_len, embed_dim = lprobs.size()
268
+ eos_indices = target.eq(self.task.tgt_dict.eos())
269
+ lprobs = lprobs[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
270
+ target = target[~eos_indices].reshape(bsz, seq_len-1)
271
+ if constraint_masks is not None:
272
+ constraint_masks = constraint_masks[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
273
+ if constraint_masks is not None:
274
+ constraint_masks = constraint_masks.view(-1, constraint_masks.size(-1))
275
+ return lprobs.view(-1, lprobs.size(-1)), target.view(-1), constraint_masks
276
+
277
+ def compute_loss(self, model, net_output, sample, update_num, reduce=True):
278
+ lprobs, target, constraint_masks = self.get_lprobs_and_target(model, net_output, sample)
279
+ if constraint_masks is not None:
280
+ constraint_masks = constraint_masks[target != self.padding_idx]
281
+ lprobs = lprobs[target != self.padding_idx]
282
+ target = target[target != self.padding_idx]
283
+ loss, nll_loss, ntokens, lprobs, target = label_smoothed_nll_loss(
284
+ lprobs,
285
+ target,
286
+ self.eps,
287
+ update_num,
288
+ reduce=reduce,
289
+ drop_worst_ratio=self.drop_worst_ratio,
290
+ drop_worst_after=self.drop_worst_after,
291
+ use_rdrop=self.use_rdrop,
292
+ reg_alpha=self.reg_alpha,
293
+ constraint_masks=constraint_masks,
294
+ constraint_start=self.constraint_start,
295
+ constraint_end=self.constraint_end
296
+ )
297
+ # for encouraging loss
298
+ probs = torch.exp(lprobs)
299
+ bonus = torch.log(torch.clamp((torch.ones_like(probs) - probs), min=1e-5)) # likelihood bonus
300
+ log_end = self.log_end
301
+ if log_end != 1.0: # e.g. 0.9
302
+ y_log_end = torch.log(torch.ones_like(probs) - log_end)
303
+ bonus_after_log_end = 1 / (log_end - torch.ones_like(probs)) * (probs - log_end) + y_log_end
304
+ # x:log_end, y torch.log(torch.clamp((torch.ones_like(probs) - probs), min=self.cl_eps))
305
+ bonus = torch.where(probs > log_end, bonus_after_log_end, bonus)
306
+ c_loss = F.nll_loss(
307
+ -bonus,
308
+ target.view(-1),
309
+ reduction='sum',
310
+ )
311
+ smoothing_c_loss = bonus.sum(dim=-1)
312
+ smoothing_c_loss = smoothing_c_loss.sum()
313
+ c_loss = c_loss * (1 - self.eps) + (self.eps / lprobs.size(-1)) * smoothing_c_loss
314
+ loss = loss + c_loss
315
+ # end for encouraging loss
316
+ return loss, nll_loss, ntokens
317
+
318
+ def compute_accuracy(self, model, net_output, sample):
319
+ lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
320
+ mask = target.ne(self.padding_idx)
321
+ n_correct = torch.sum(
322
+ lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
323
+ )
324
+ total = torch.sum(mask)
325
+ return n_correct, total
326
+
327
+ @classmethod
328
+ def reduce_metrics(cls, logging_outputs) -> None:
329
+ """Aggregate logging outputs from data parallel training."""
330
+ loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
331
+ loss_sum_v1 = sum(log.get("loss_v1", 0) for log in logging_outputs)
332
+ loss_sum_v2 = sum(log.get("loss_v2", 0) for log in logging_outputs)
333
+ nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
334
+ ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
335
+ nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
336
+ sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
337
+ sample_size_v1 = sum(log.get("sample_size_v1", 0) for log in logging_outputs)
338
+ sample_size_v2 = sum(log.get("sample_size_v2", 0) for log in logging_outputs)
339
+
340
+ metrics.log_scalar(
341
+ "loss", loss_sum / sample_size, sample_size, round=3
342
+ )
343
+ metrics.log_scalar(
344
+ "loss_v1", loss_sum_v1 / max(sample_size_v1, 1), max(sample_size_v1, 1), round=3
345
+ )
346
+ metrics.log_scalar(
347
+ "loss_v2", loss_sum_v2 / max(sample_size_v2, 1), max(sample_size_v2, 1), round=3
348
+ )
349
+ metrics.log_scalar(
350
+ "nll_loss", nll_loss_sum / sample_size, ntokens, round=3
351
+ )
352
+ metrics.log_derived(
353
+ "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
354
+ )
355
+
356
+ metrics.log_scalar(
357
+ "ntokens", ntokens, 1, round=3
358
+ )
359
+ metrics.log_scalar(
360
+ "nsentences", nsentences, 1, round=3
361
+ )
362
+ metrics.log_scalar(
363
+ "sample_size", sample_size, 1, round=3
364
+ )
365
+ metrics.log_scalar(
366
+ "sample_size_v1", sample_size_v1, 1, round=3
367
+ )
368
+ metrics.log_scalar(
369
+ "sample_size_v2", sample_size_v2, 1, round=3
370
+ )
371
+
372
+ total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
373
+ if total > 0:
374
+ metrics.log_scalar("total", total)
375
+ n_correct = utils.item(
376
+ sum(log.get("n_correct", 0) for log in logging_outputs)
377
+ )
378
+ metrics.log_scalar("n_correct", n_correct)
379
+ metrics.log_derived(
380
+ "accuracy",
381
+ lambda meters: round(
382
+ meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
383
+ )
384
+ if meters["total"].sum > 0
385
+ else float("nan"),
386
+ )
387
+
388
+ @staticmethod
389
+ def logging_outputs_can_be_summed() -> bool:
390
+ """
391
+ Whether the logging outputs returned by `forward` can be summed
392
+ across workers prior to calling `reduce_metrics`. Setting this
393
+ to True will improves distributed training speed.
394
+ """
395
+ return True
criterions/scst_loss.py ADDED
@@ -0,0 +1,281 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import math
7
+ import string
8
+ from dataclasses import dataclass, field
9
+ from collections import OrderedDict
10
+ from typing import Optional
11
+
12
+ import torch
13
+ from fairseq import metrics, utils
14
+ from fairseq.criterions import FairseqCriterion, register_criterion
15
+ from fairseq.dataclass import FairseqDataclass
16
+ from omegaconf import II
17
+
18
+ from data import data_utils
19
+ from utils.cider.pyciderevalcap.ciderD.ciderD import CiderD
20
+
21
+
22
+ def scst_loss(lprobs, target, reward, ignore_index=None, reduce=True):
23
+ loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward.unsqueeze(-1)
24
+ if ignore_index is not None:
25
+ pad_mask = target.eq(ignore_index)
26
+ loss.masked_fill_(pad_mask, 0.0)
27
+ ntokens = (~pad_mask).sum()
28
+ else:
29
+ loss = loss.squeeze(-1)
30
+ ntokens = target.numel()
31
+ if reduce:
32
+ loss = loss.sum()
33
+ return loss, ntokens
34
+
35
+
36
+ @dataclass
37
+ class ScstRewardCriterionConfig(FairseqDataclass):
38
+ scst_cider_cached_tokens: str = field(
39
+ default="coco-train-words.p",
40
+ metadata={"help": "path to cached cPickle file used to calculate CIDEr scores"},
41
+ )
42
+ ignore_prefix_size: int = field(
43
+ default=0,
44
+ metadata={"help": "Ignore first N tokens"},
45
+ )
46
+ sentence_avg: bool = II("optimization.sentence_avg")
47
+ constraint_range: Optional[str] = field(
48
+ default=None,
49
+ metadata={"help": "constraint range"}
50
+ )
51
+
52
+
53
+ @register_criterion(
54
+ "scst_reward_criterion", dataclass=ScstRewardCriterionConfig
55
+ )
56
+ class ScstRewardCriterion(FairseqCriterion):
57
+ CIDER_REWARD_WEIGHT = 1
58
+
59
+ def __init__(
60
+ self,
61
+ task,
62
+ scst_cider_cached_tokens,
63
+ sentence_avg,
64
+ ignore_prefix_size=0,
65
+ constraint_range=None
66
+ ):
67
+ super().__init__(task)
68
+ self.scst_cider_scorer = CiderD(df=scst_cider_cached_tokens)
69
+ self.sentence_avg = sentence_avg
70
+ self.ignore_prefix_size = ignore_prefix_size
71
+ self.transtab = str.maketrans({key: None for key in string.punctuation})
72
+
73
+ self.constraint_start = None
74
+ self.constraint_end = None
75
+ if constraint_range is not None:
76
+ constraint_start, constraint_end = constraint_range.split(',')
77
+ self.constraint_start = int(constraint_start)
78
+ self.constraint_end = int(constraint_end)
79
+
80
+ def forward(self, model, sample, update_num=0, reduce=True):
81
+ """Compute the loss for the given sample.
82
+
83
+ Returns a tuple with three elements:
84
+ 1) the loss
85
+ 2) the sample size, which is used as the denominator for the gradient
86
+ 3) logging outputs to display while training
87
+ """
88
+ loss, score, ntokens, nsentences = self.compute_loss(model, sample, reduce=reduce)
89
+
90
+ sample_size = (
91
+ nsentences if self.sentence_avg else ntokens
92
+ )
93
+ logging_output = {
94
+ "loss": loss.data,
95
+ "score": score,
96
+ "ntokens": ntokens,
97
+ "nsentences": nsentences,
98
+ "sample_size": sample_size,
99
+ }
100
+ return loss, sample_size, logging_output
101
+
102
+ def _calculate_eval_scores(self, gen_res, gt_idx, gt_res):
103
+ '''
104
+ gen_res: generated captions, list of str
105
+ gt_idx: list of int, of the same length as gen_res
106
+ gt_res: ground truth captions, list of list of str.
107
+ gen_res[i] corresponds to gt_res[gt_idx[i]]
108
+ Each image can have multiple ground truth captions
109
+ '''
110
+ gen_res_size = len(gen_res)
111
+
112
+ res = OrderedDict()
113
+ for i in range(gen_res_size):
114
+ res[i] = [self._wrap_sentence(gen_res[i].strip().translate(self.transtab))]
115
+
116
+ gts = OrderedDict()
117
+ gt_res_ = [
118
+ [self._wrap_sentence(gt_res[i][j].strip().translate(self.transtab)) for j in range(len(gt_res[i]))]
119
+ for i in range(len(gt_res))
120
+ ]
121
+ for i in range(gen_res_size):
122
+ gts[i] = gt_res_[gt_idx[i]]
123
+
124
+ res_ = [{'image_id':i, 'caption': res[i]} for i in range(len(res))]
125
+ _, batch_cider_scores = self.scst_cider_scorer.compute_score(gts, res_)
126
+ scores = self.CIDER_REWARD_WEIGHT * batch_cider_scores
127
+ return scores
128
+
129
+ @classmethod
130
+ def _wrap_sentence(self, s):
131
+ # ensure the sentence ends with <eos> token
132
+ # in order to keep consisitent with cider_cached_tokens
133
+ r = s.strip()
134
+ if r.endswith('.'):
135
+ r = r[:-1]
136
+ r += ' <eos>'
137
+ return r
138
+
139
+ def get_generator_out(self, model, sample):
140
+ def decode(toks):
141
+ hypo = toks.int().cpu()
142
+ hypo_str = self.task.tgt_dict.string(hypo)
143
+ hypo_str = self.task.bpe.decode(hypo_str).strip()
144
+ return hypo, hypo_str
145
+
146
+ model.eval()
147
+ with torch.no_grad():
148
+ self.task.scst_generator.model.eval()
149
+ gen_out = self.task.scst_generator.generate([model], sample)
150
+
151
+ gen_target = []
152
+ gen_res = []
153
+ gt_res = []
154
+ for i in range(len(gen_out)):
155
+ for j in range(len(gen_out[i])):
156
+ hypo, hypo_str = decode(gen_out[i][j]["tokens"])
157
+ gen_target.append(hypo)
158
+ gen_res.append(hypo_str)
159
+ gt_res.append(
160
+ decode(utils.strip_pad(sample["target"][i], self.padding_idx))[1].split('&&')
161
+ )
162
+
163
+ return gen_target, gen_res, gt_res
164
+
165
+ def get_reward_and_scores(self, gen_res, gt_res, device):
166
+ batch_size = len(gt_res)
167
+ gen_res_size = len(gen_res)
168
+ seq_per_img = gen_res_size // batch_size
169
+
170
+ gt_idx = [i // seq_per_img for i in range(gen_res_size)]
171
+ scores = self._calculate_eval_scores(gen_res, gt_idx, gt_res)
172
+ sc_ = scores.reshape(batch_size, seq_per_img)
173
+ baseline = (sc_.sum(1, keepdims=True) - sc_) / (sc_.shape[1] - 1)
174
+ # sample - baseline
175
+ reward = scores.reshape(batch_size, seq_per_img)
176
+ reward = reward - baseline
177
+ reward = reward.reshape(gen_res_size)
178
+ reward = torch.as_tensor(reward, device=device, dtype=torch.float64)
179
+
180
+ return reward, scores
181
+
182
+ def get_net_output(self, model, sample, gen_target):
183
+ def merge(sample_list, eos=self.task.tgt_dict.eos(), move_eos_to_beginning=False):
184
+ return data_utils.collate_tokens(
185
+ sample_list,
186
+ pad_idx=self.padding_idx,
187
+ eos_idx=eos,
188
+ left_pad=False,
189
+ move_eos_to_beginning=move_eos_to_beginning,
190
+ )
191
+
192
+ batch_size = len(sample["target"])
193
+ gen_target_size = len(gen_target)
194
+ seq_per_img = gen_target_size // batch_size
195
+
196
+ model.train()
197
+ sample_src_tokens = torch.repeat_interleave(
198
+ sample['net_input']['src_tokens'], seq_per_img, dim=0
199
+ )
200
+ sample_src_lengths = torch.repeat_interleave(
201
+ sample['net_input']['src_lengths'], seq_per_img, dim=0
202
+ )
203
+ sample_patch_images = torch.repeat_interleave(
204
+ sample['net_input']['patch_images'], seq_per_img, dim=0
205
+ )
206
+ sample_patch_masks = torch.repeat_interleave(
207
+ sample['net_input']['patch_masks'], seq_per_img, dim=0
208
+ )
209
+ gen_prev_output_tokens = torch.as_tensor(
210
+ merge(gen_target, eos=self.task.tgt_dict.bos(), move_eos_to_beginning=True),
211
+ device=sample["target"].device, dtype=torch.int64
212
+ )
213
+ gen_target_tokens = torch.as_tensor(
214
+ merge(gen_target), device=sample["target"].device, dtype=torch.int64
215
+ )
216
+ net_output = model(
217
+ src_tokens=sample_src_tokens, src_lengths=sample_src_lengths,
218
+ patch_images=sample_patch_images, patch_masks=sample_patch_masks,
219
+ prev_output_tokens=gen_prev_output_tokens
220
+ )
221
+
222
+ return net_output, gen_target_tokens
223
+
224
+ def get_lprobs_and_target(self, model, net_output, gen_target):
225
+ if self.constraint_start is not None and self.constraint_end is not None:
226
+ net_output[0][:, :, 4:self.constraint_start] = -math.inf
227
+ net_output[0][:, :, self.constraint_end:] = -math.inf
228
+ lprobs = model.get_normalized_probs(net_output, log_probs=True)
229
+ if self.ignore_prefix_size > 0:
230
+ if getattr(lprobs, "batch_first", False):
231
+ lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
232
+ gen_target = gen_target[:, self.ignore_prefix_size :].contiguous()
233
+ else:
234
+ lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
235
+ gen_target = gen_target[self.ignore_prefix_size :, :].contiguous()
236
+ return lprobs, gen_target
237
+
238
+ def compute_loss(self, model, sample, reduce=True):
239
+ gen_target, gen_res, gt_res = self.get_generator_out(model, sample)
240
+ reward, scores = self.get_reward_and_scores(gen_res, gt_res, device=sample["target"].device)
241
+ net_output, gen_target_tokens = self.get_net_output(model, sample, gen_target)
242
+ gen_lprobs, gen_target_tokens = self.get_lprobs_and_target(model, net_output, gen_target_tokens)
243
+ loss, ntokens = scst_loss(gen_lprobs, gen_target_tokens, reward, ignore_index=self.padding_idx, reduce=reduce)
244
+ nsentences = gen_target_tokens.size(0)
245
+
246
+ return loss, scores.sum(), ntokens, nsentences
247
+
248
+ @classmethod
249
+ def reduce_metrics(cls, logging_outputs) -> None:
250
+ """Aggregate logging outputs from data parallel training."""
251
+ loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
252
+ score_sum = sum(log.get("score", 0) for log in logging_outputs)
253
+ ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
254
+ nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
255
+ sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
256
+
257
+ metrics.log_scalar(
258
+ "loss", loss_sum / sample_size, sample_size, round=3
259
+ )
260
+ metrics.log_scalar(
261
+ "score", score_sum / nsentences, nsentences, round=3
262
+ )
263
+
264
+ metrics.log_scalar(
265
+ "ntokens", ntokens, 1, round=3
266
+ )
267
+ metrics.log_scalar(
268
+ "nsentences", nsentences, 1, round=3
269
+ )
270
+ metrics.log_scalar(
271
+ "sample_size", sample_size, 1, round=3
272
+ )
273
+
274
+ @staticmethod
275
+ def logging_outputs_can_be_summed() -> bool:
276
+ """
277
+ Whether the logging outputs returned by `forward` can be summed
278
+ across workers prior to calling `reduce_metrics`. Setting this
279
+ to True will improves distributed training speed.
280
+ """
281
+ return True
data/__init__.py ADDED
File without changes
data/cv_data/image_classify_dataset.py ADDED
@@ -0,0 +1,196 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import logging
9
+ import warnings
10
+ import functools
11
+
12
+ import numpy as np
13
+ import torch
14
+ import base64
15
+ from torchvision import transforms
16
+ from timm.data import create_transform
17
+ from utils.vision_helper import RandomAugment
18
+
19
+ from PIL import Image, ImageFile
20
+
21
+ from data import data_utils
22
+ from data.ofa_dataset import OFADataset
23
+
24
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
25
+ ImageFile.MAX_IMAGE_PIXELS = None
26
+ Image.MAX_IMAGE_PIXELS = None
27
+
28
+ logger = logging.getLogger(__name__)
29
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
30
+
31
+ IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
32
+ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
33
+
34
+ def collate(samples, pad_idx, eos_idx):
35
+ if len(samples) == 0:
36
+ return {}
37
+
38
+ def merge(key):
39
+ return data_utils.collate_tokens(
40
+ [s[key] for s in samples],
41
+ pad_idx,
42
+ eos_idx=eos_idx,
43
+ )
44
+
45
+ id = np.array([s["id"] for s in samples])
46
+ src_tokens = merge("source")
47
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
48
+
49
+ patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
50
+ patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
51
+
52
+ conf = None
53
+ if samples[0].get("conf", None) is not None:
54
+ conf = torch.cat([s['conf'] for s in samples], dim=0)
55
+
56
+ ref_dict = None
57
+ if samples[0].get("ref_dict", None) is not None:
58
+ ref_dict = np.array([s['ref_dict'] for s in samples])
59
+
60
+ constraint_masks = None
61
+ if samples[0].get("constraint_mask", None) is not None:
62
+ constraint_masks = merge("constraint_mask")
63
+
64
+ prev_output_tokens = None
65
+ target = None
66
+ if samples[0].get("target", None) is not None:
67
+ target = merge("target")
68
+ tgt_lengths = torch.LongTensor(
69
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
70
+ )
71
+ ntokens = tgt_lengths.sum().item()
72
+
73
+ if samples[0].get("prev_output_tokens", None) is not None:
74
+ prev_output_tokens = merge("prev_output_tokens")
75
+ else:
76
+ ntokens = src_lengths.sum().item()
77
+
78
+ batch = {
79
+ "id": id,
80
+ "nsentences": len(samples),
81
+ "ntokens": ntokens,
82
+ "net_input": {
83
+ "src_tokens": src_tokens,
84
+ "src_lengths": src_lengths,
85
+ "patch_images": patch_images,
86
+ "patch_masks": patch_masks,
87
+ "prev_output_tokens": prev_output_tokens
88
+ },
89
+ "conf": conf,
90
+ "ref_dict": ref_dict,
91
+ "constraint_masks": constraint_masks,
92
+ "target": target,
93
+ }
94
+
95
+ return batch
96
+
97
+
98
+ class ImageClassifyDataset(OFADataset):
99
+ def __init__(
100
+ self,
101
+ split,
102
+ dataset,
103
+ bpe,
104
+ src_dict,
105
+ tgt_dict=None,
106
+ max_src_length=128,
107
+ max_tgt_length=30,
108
+ patch_image_size=224,
109
+ constraint_trie=None,
110
+ imagenet_default_mean_and_std=False
111
+ ):
112
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
113
+ self.max_src_length = max_src_length
114
+ self.max_tgt_length = max_tgt_length
115
+ self.patch_image_size = patch_image_size
116
+
117
+ self.constraint_trie = constraint_trie
118
+
119
+ if imagenet_default_mean_and_std:
120
+ mean = IMAGENET_DEFAULT_MEAN
121
+ std = IMAGENET_DEFAULT_STD
122
+ else:
123
+ mean = [0.5, 0.5, 0.5]
124
+ std = [0.5, 0.5, 0.5]
125
+
126
+ if self.split != 'train':
127
+ self.patch_resize_transform = transforms.Compose([
128
+ lambda image: image.convert("RGB"),
129
+ transforms.Resize([patch_image_size, patch_image_size], interpolation=Image.BICUBIC),
130
+ transforms.ToTensor(),
131
+ transforms.Normalize(mean=mean, std=std),
132
+ ])
133
+ logger.info("val split, do not use random augmentation.")
134
+ else:
135
+ self.patch_resize_transform = create_transform(
136
+ input_size=patch_image_size,
137
+ is_training=True,
138
+ color_jitter=0.4,
139
+ auto_augment='rand-m9-mstd0.5-inc1',
140
+ interpolation='bicubic',
141
+ re_prob=0.25,
142
+ re_mode='pixel',
143
+ re_count=1,
144
+ mean=mean,
145
+ std=std,
146
+ )
147
+ self.patch_resize_transform = transforms.Compose(functools.reduce(lambda x, y:x + y, [
148
+ [lambda image: image.convert("RGB"),],
149
+ self.patch_resize_transform.transforms[:2],
150
+ [self.patch_resize_transform.transforms[2]],
151
+ [RandomAugment(2, 7, isPIL=True, augs=['Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness', 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']), ],
152
+ self.patch_resize_transform.transforms[3:],
153
+ ]))
154
+ logger.info("train split, use random augmentation.")
155
+
156
+ def __getitem__(self, index):
157
+ image, label_name = self.dataset[index]
158
+
159
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
160
+ patch_image = self.patch_resize_transform(image)
161
+ patch_mask = torch.tensor([True])
162
+
163
+ src_item = self.encode_text(' what does the image describe?')
164
+ tgt_item = self.encode_text(" {}".format(label_name))
165
+ ref_dict = {label_name: 1.0}
166
+
167
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
168
+ target_item = torch.cat([tgt_item, self.eos_item])
169
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
170
+
171
+ example = {
172
+ "id": index,
173
+ "source": src_item,
174
+ "patch_image": patch_image,
175
+ "patch_mask": patch_mask,
176
+ "target": target_item,
177
+ "prev_output_tokens": prev_output_item,
178
+ "ref_dict": ref_dict,
179
+ }
180
+ if self.constraint_trie is not None:
181
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
182
+ for i in range(len(prev_output_item)):
183
+ constraint_prefix_token = prev_output_item[:i+1].tolist()
184
+ constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
185
+ constraint_mask[i][constraint_nodes] = True
186
+ example["constraint_mask"] = constraint_mask
187
+ return example
188
+
189
+ def collater(self, samples, pad_to_length=None):
190
+ """Merge a list of samples to form a mini-batch.
191
+ Args:
192
+ samples (List[dict]): samples to collate
193
+ Returns:
194
+ dict: a mini-batch containing the data of the task
195
+ """
196
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/data_utils.py ADDED
@@ -0,0 +1,601 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ try:
7
+ from collections.abc import Iterable
8
+ except ImportError:
9
+ from collections import Iterable
10
+ import contextlib
11
+ import itertools
12
+ import logging
13
+ import re
14
+ import warnings
15
+ from typing import Optional, Tuple
16
+
17
+ import numpy as np
18
+ import torch
19
+
20
+ from fairseq.file_io import PathManager
21
+ from fairseq import utils
22
+ import os
23
+
24
+ logger = logging.getLogger(__name__)
25
+
26
+
27
+ def infer_language_pair(path):
28
+ """Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
29
+ src, dst = None, None
30
+ for filename in PathManager.ls(path):
31
+ parts = filename.split(".")
32
+ if len(parts) >= 3 and len(parts[1].split("-")) == 2:
33
+ return parts[1].split("-")
34
+ return src, dst
35
+
36
+
37
+ def collate_tokens(
38
+ values,
39
+ pad_idx,
40
+ eos_idx=None,
41
+ left_pad=False,
42
+ move_eos_to_beginning=False,
43
+ pad_to_length=None,
44
+ pad_to_multiple=1,
45
+ pad_to_bsz=None,
46
+ ):
47
+ """Convert a list of 1d tensors into a padded 2d tensor."""
48
+ size = max(v.size(0) for v in values)
49
+ size = size if pad_to_length is None else max(size, pad_to_length)
50
+ if pad_to_multiple != 1 and size % pad_to_multiple != 0:
51
+ size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
52
+
53
+ def copy_tensor(src, dst):
54
+ assert dst.numel() == src.numel()
55
+ if move_eos_to_beginning:
56
+ if eos_idx is None:
57
+ # if no eos_idx is specified, then use the last token in src
58
+ dst[0] = src[-1]
59
+ else:
60
+ dst[0] = eos_idx
61
+ dst[1:] = src[:-1]
62
+ else:
63
+ dst.copy_(src)
64
+
65
+ if values[0].dim() == 1:
66
+ res = values[0].new(len(values), size).fill_(pad_idx)
67
+ elif values[0].dim() == 2:
68
+ assert move_eos_to_beginning is False
69
+ res = values[0].new(len(values), size, values[0].size(1)).fill_(pad_idx)
70
+ else:
71
+ raise NotImplementedError
72
+
73
+ for i, v in enumerate(values):
74
+ copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
75
+ return res
76
+
77
+
78
+ def load_indexed_dataset(
79
+ path, dictionary=None, dataset_impl=None, combine=False, default="cached"
80
+ ):
81
+ """A helper function for loading indexed datasets.
82
+
83
+ Args:
84
+ path (str): path to indexed dataset (e.g., 'data-bin/train')
85
+ dictionary (~fairseq.data.Dictionary): data dictionary
86
+ dataset_impl (str, optional): which dataset implementation to use. If
87
+ not provided, it will be inferred automatically. For legacy indexed
88
+ data we use the 'cached' implementation by default.
89
+ combine (bool, optional): automatically load and combine multiple
90
+ datasets. For example, if *path* is 'data-bin/train', then we will
91
+ combine 'data-bin/train', 'data-bin/train1', ... and return a
92
+ single ConcatDataset instance.
93
+ """
94
+ import fairseq.data.indexed_dataset as indexed_dataset
95
+ from fairseq.data.concat_dataset import ConcatDataset
96
+
97
+ datasets = []
98
+ for k in itertools.count():
99
+ path_k = path + (str(k) if k > 0 else "")
100
+ try:
101
+ path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
102
+ except Exception as e:
103
+ if "StorageException: [404] Path not found" in str(e):
104
+ logger.warning(f"path_k: {e} not found")
105
+ else:
106
+ raise e
107
+
108
+ dataset_impl_k = dataset_impl
109
+ if dataset_impl_k is None:
110
+ dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
111
+ dataset = indexed_dataset.make_dataset(
112
+ path_k,
113
+ impl=dataset_impl_k or default,
114
+ fix_lua_indexing=True,
115
+ dictionary=dictionary,
116
+ )
117
+ if dataset is None:
118
+ break
119
+ logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
120
+ datasets.append(dataset)
121
+ if not combine:
122
+ break
123
+ if len(datasets) == 0:
124
+ return None
125
+ elif len(datasets) == 1:
126
+ return datasets[0]
127
+ else:
128
+ return ConcatDataset(datasets)
129
+
130
+
131
+ @contextlib.contextmanager
132
+ def numpy_seed(seed, *addl_seeds):
133
+ """Context manager which seeds the NumPy PRNG with the specified seed and
134
+ restores the state afterward"""
135
+ if seed is None:
136
+ yield
137
+ return
138
+ if len(addl_seeds) > 0:
139
+ seed = int(hash((seed, *addl_seeds)) % 1e6)
140
+ state = np.random.get_state()
141
+ np.random.seed(seed)
142
+ try:
143
+ yield
144
+ finally:
145
+ np.random.set_state(state)
146
+
147
+
148
+ def collect_filtered(function, iterable, filtered):
149
+ """
150
+ Similar to :func:`filter` but collects filtered elements in ``filtered``.
151
+
152
+ Args:
153
+ function (callable): function that returns ``False`` for elements that
154
+ should be filtered
155
+ iterable (iterable): iterable to filter
156
+ filtered (list): list to store filtered elements
157
+ """
158
+ for el in iterable:
159
+ if function(el):
160
+ yield el
161
+ else:
162
+ filtered.append(el)
163
+
164
+
165
+ def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
166
+ def compare_leq(a, b):
167
+ return a <= b if not isinstance(a, tuple) else max(a) <= b
168
+
169
+ def check_size(idx):
170
+ if isinstance(max_positions, float) or isinstance(max_positions, int):
171
+ return size_fn(idx) <= max_positions
172
+ elif isinstance(max_positions, dict):
173
+ idx_size = size_fn(idx)
174
+ assert isinstance(idx_size, dict)
175
+ intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
176
+ return all(
177
+ all(
178
+ a is None or b is None or a <= b
179
+ for a, b in zip(idx_size[key], max_positions[key])
180
+ )
181
+ for key in intersect_keys
182
+ )
183
+ else:
184
+ # For MultiCorpusSampledDataset, will generalize it later
185
+ if not isinstance(size_fn(idx), Iterable):
186
+ return all(size_fn(idx) <= b for b in max_positions)
187
+ return all(
188
+ a is None or b is None or a <= b
189
+ for a, b in zip(size_fn(idx), max_positions)
190
+ )
191
+
192
+ ignored = []
193
+ itr = collect_filtered(check_size, indices, ignored)
194
+ indices = np.fromiter(itr, dtype=np.int64, count=-1)
195
+ return indices, ignored
196
+
197
+
198
+ def filter_by_size(indices, dataset, max_positions, raise_exception=False):
199
+ """
200
+ [deprecated] Filter indices based on their size.
201
+ Use `FairseqDataset::filter_indices_by_size` instead.
202
+
203
+ Args:
204
+ indices (List[int]): ordered list of dataset indices
205
+ dataset (FairseqDataset): fairseq dataset instance
206
+ max_positions (tuple): filter elements larger than this size.
207
+ Comparisons are done component-wise.
208
+ raise_exception (bool, optional): if ``True``, raise an exception if
209
+ any elements are filtered (default: False).
210
+ """
211
+ warnings.warn(
212
+ "data_utils.filter_by_size is deprecated. "
213
+ "Use `FairseqDataset::filter_indices_by_size` instead.",
214
+ stacklevel=2,
215
+ )
216
+ if isinstance(max_positions, float) or isinstance(max_positions, int):
217
+ if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
218
+ ignored = indices[dataset.sizes[indices] > max_positions].tolist()
219
+ indices = indices[dataset.sizes[indices] <= max_positions]
220
+ elif (
221
+ hasattr(dataset, "sizes")
222
+ and isinstance(dataset.sizes, list)
223
+ and len(dataset.sizes) == 1
224
+ ):
225
+ ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
226
+ indices = indices[dataset.sizes[0][indices] <= max_positions]
227
+ else:
228
+ indices, ignored = _filter_by_size_dynamic(
229
+ indices, dataset.size, max_positions
230
+ )
231
+ else:
232
+ indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
233
+
234
+ if len(ignored) > 0 and raise_exception:
235
+ raise Exception(
236
+ (
237
+ "Size of sample #{} is invalid (={}) since max_positions={}, "
238
+ "skip this example with --skip-invalid-size-inputs-valid-test"
239
+ ).format(ignored[0], dataset.size(ignored[0]), max_positions)
240
+ )
241
+ if len(ignored) > 0:
242
+ logger.warning(
243
+ (
244
+ "{} samples have invalid sizes and will be skipped, "
245
+ "max_positions={}, first few sample ids={}"
246
+ ).format(len(ignored), max_positions, ignored[:10])
247
+ )
248
+ return indices
249
+
250
+
251
+ def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
252
+ """Filter a list of sample indices. Remove those that are longer
253
+ than specified in max_sizes.
254
+
255
+ Args:
256
+ indices (np.array): original array of sample indices
257
+ max_sizes (int or list[int] or tuple[int]): max sample size,
258
+ can be defined separately for src and tgt (then list or tuple)
259
+
260
+ Returns:
261
+ np.array: filtered sample array
262
+ list: list of removed indices
263
+ """
264
+ if max_sizes is None:
265
+ return indices, []
266
+ if type(max_sizes) in (int, float):
267
+ max_src_size, max_tgt_size = max_sizes, max_sizes
268
+ else:
269
+ max_src_size, max_tgt_size = max_sizes
270
+ if tgt_sizes is None:
271
+ ignored = indices[src_sizes[indices] > max_src_size]
272
+ else:
273
+ ignored = indices[
274
+ (src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
275
+ ]
276
+ if len(ignored) > 0:
277
+ if tgt_sizes is None:
278
+ indices = indices[src_sizes[indices] <= max_src_size]
279
+ else:
280
+ indices = indices[
281
+ (src_sizes[indices] <= max_src_size)
282
+ & (tgt_sizes[indices] <= max_tgt_size)
283
+ ]
284
+ return indices, ignored.tolist()
285
+
286
+
287
+ def batch_by_size(
288
+ indices,
289
+ num_tokens_fn,
290
+ num_tokens_vec=None,
291
+ max_tokens=None,
292
+ max_sentences=None,
293
+ required_batch_size_multiple=1,
294
+ fixed_shapes=None,
295
+ ):
296
+ """
297
+ Yield mini-batches of indices bucketed by size. Batches may contain
298
+ sequences of different lengths.
299
+
300
+ Args:
301
+ indices (List[int]): ordered list of dataset indices
302
+ num_tokens_fn (callable): function that returns the number of tokens at
303
+ a given index
304
+ num_tokens_vec (List[int], optional): precomputed vector of the number
305
+ of tokens for each index in indices (to enable faster batch generation)
306
+ max_tokens (int, optional): max number of tokens in each batch
307
+ (default: None).
308
+ max_sentences (int, optional): max number of sentences in each
309
+ batch (default: None).
310
+ required_batch_size_multiple (int, optional): require batch size to
311
+ be less than N or a multiple of N (default: 1).
312
+ fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
313
+ only be created with the given shapes. *max_sentences* and
314
+ *required_batch_size_multiple* will be ignored (default: None).
315
+ """
316
+ try:
317
+ from fairseq.data.data_utils_fast import (
318
+ batch_by_size_fn,
319
+ batch_by_size_vec,
320
+ batch_fixed_shapes_fast,
321
+ )
322
+ except ImportError:
323
+ raise ImportError(
324
+ "Please build Cython components with: "
325
+ "`python setup.py build_ext --inplace`"
326
+ )
327
+ except ValueError:
328
+ raise ValueError(
329
+ "Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`."
330
+ )
331
+
332
+ # added int() to avoid TypeError: an integer is required
333
+ max_tokens = (
334
+ int(max_tokens) if max_tokens is not None else -1
335
+ )
336
+ max_sentences = max_sentences if max_sentences is not None else -1
337
+ bsz_mult = required_batch_size_multiple
338
+
339
+ if not isinstance(indices, np.ndarray):
340
+ indices = np.fromiter(indices, dtype=np.int64, count=-1)
341
+
342
+ if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
343
+ num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)
344
+
345
+ if fixed_shapes is None:
346
+ if num_tokens_vec is None:
347
+ return batch_by_size_fn(
348
+ indices,
349
+ num_tokens_fn,
350
+ max_tokens,
351
+ max_sentences,
352
+ bsz_mult,
353
+ )
354
+ else:
355
+ return batch_by_size_vec(
356
+ indices,
357
+ num_tokens_vec,
358
+ max_tokens,
359
+ max_sentences,
360
+ bsz_mult,
361
+ )
362
+
363
+ else:
364
+ fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
365
+ sort_order = np.lexsort(
366
+ [
367
+ fixed_shapes[:, 1].argsort(), # length
368
+ fixed_shapes[:, 0].argsort(), # bsz
369
+ ]
370
+ )
371
+ fixed_shapes_sorted = fixed_shapes[sort_order]
372
+ return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)
373
+
374
+
375
+ def post_process(sentence: str, symbol: str):
376
+ if symbol == "sentencepiece":
377
+ sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
378
+ elif symbol == "wordpiece":
379
+ sentence = sentence.replace(" ", "").replace("_", " ").strip()
380
+ elif symbol == "letter":
381
+ sentence = sentence.replace(" ", "").replace("|", " ").strip()
382
+ elif symbol == "silence":
383
+ import re
384
+ sentence = sentence.replace("<SIL>", "")
385
+ sentence = re.sub(' +', ' ', sentence).strip()
386
+ elif symbol == "_EOW":
387
+ sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
388
+ elif symbol in {"subword_nmt", "@@ ", "@@"}:
389
+ if symbol == "subword_nmt":
390
+ symbol = "@@ "
391
+ sentence = (sentence + " ").replace(symbol, "").rstrip()
392
+ elif symbol == "none":
393
+ pass
394
+ elif symbol is not None:
395
+ raise NotImplementedError(f"Unknown post_process option: {symbol}")
396
+ return sentence
397
+
398
+
399
+ def compute_mask_indices(
400
+ shape: Tuple[int, int],
401
+ padding_mask: Optional[torch.Tensor],
402
+ mask_prob: float,
403
+ mask_length: int,
404
+ mask_type: str = "static",
405
+ mask_other: float = 0.0,
406
+ min_masks: int = 0,
407
+ no_overlap: bool = False,
408
+ min_space: int = 0,
409
+ ) -> np.ndarray:
410
+ """
411
+ Computes random mask spans for a given shape
412
+
413
+ Args:
414
+ shape: the the shape for which to compute masks.
415
+ should be of size 2 where first element is batch size and 2nd is timesteps
416
+ padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
417
+ mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
418
+ number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
419
+ however due to overlaps, the actual number will be smaller (unless no_overlap is True)
420
+ mask_type: how to compute mask lengths
421
+ static = fixed size
422
+ uniform = sample from uniform distribution [mask_other, mask_length*2]
423
+ normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
424
+ poisson = sample from possion distribution with lambda = mask length
425
+ min_masks: minimum number of masked spans
426
+ no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
427
+ min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
428
+ """
429
+
430
+ bsz, all_sz = shape
431
+ mask = np.full((bsz, all_sz), False)
432
+
433
+ all_num_mask = int(
434
+ # add a random number for probabilistic rounding
435
+ mask_prob * all_sz / float(mask_length)
436
+ + np.random.rand()
437
+ )
438
+
439
+ all_num_mask = max(min_masks, all_num_mask)
440
+
441
+ mask_idcs = []
442
+ for i in range(bsz):
443
+ if padding_mask is not None:
444
+ sz = all_sz - padding_mask[i].long().sum().item()
445
+ num_mask = int(
446
+ # add a random number for probabilistic rounding
447
+ mask_prob * sz / float(mask_length)
448
+ + np.random.rand()
449
+ )
450
+ num_mask = max(min_masks, num_mask)
451
+ else:
452
+ sz = all_sz
453
+ num_mask = all_num_mask
454
+
455
+ if mask_type == "static":
456
+ lengths = np.full(num_mask, mask_length)
457
+ elif mask_type == "uniform":
458
+ lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
459
+ elif mask_type == "normal":
460
+ lengths = np.random.normal(mask_length, mask_other, size=num_mask)
461
+ lengths = [max(1, int(round(x))) for x in lengths]
462
+ elif mask_type == "poisson":
463
+ lengths = np.random.poisson(mask_length, size=num_mask)
464
+ lengths = [int(round(x)) for x in lengths]
465
+ else:
466
+ raise Exception("unknown mask selection " + mask_type)
467
+
468
+ if sum(lengths) == 0:
469
+ lengths[0] = min(mask_length, sz - 1)
470
+
471
+ if no_overlap:
472
+ mask_idc = []
473
+
474
+ def arrange(s, e, length, keep_length):
475
+ span_start = np.random.randint(s, e - length)
476
+ mask_idc.extend(span_start + i for i in range(length))
477
+
478
+ new_parts = []
479
+ if span_start - s - min_space >= keep_length:
480
+ new_parts.append((s, span_start - min_space + 1))
481
+ if e - span_start - keep_length - min_space > keep_length:
482
+ new_parts.append((span_start + length + min_space, e))
483
+ return new_parts
484
+
485
+ parts = [(0, sz)]
486
+ min_length = min(lengths)
487
+ for length in sorted(lengths, reverse=True):
488
+ lens = np.fromiter(
489
+ (e - s if e - s >= length + min_space else 0 for s, e in parts),
490
+ np.int,
491
+ )
492
+ l_sum = np.sum(lens)
493
+ if l_sum == 0:
494
+ break
495
+ probs = lens / np.sum(lens)
496
+ c = np.random.choice(len(parts), p=probs)
497
+ s, e = parts.pop(c)
498
+ parts.extend(arrange(s, e, length, min_length))
499
+ mask_idc = np.asarray(mask_idc)
500
+ else:
501
+ min_len = min(lengths)
502
+ if sz - min_len <= num_mask:
503
+ min_len = sz - num_mask - 1
504
+
505
+ mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
506
+
507
+ mask_idc = np.asarray(
508
+ [
509
+ mask_idc[j] + offset
510
+ for j in range(len(mask_idc))
511
+ for offset in range(lengths[j])
512
+ ]
513
+ )
514
+
515
+ mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
516
+
517
+ min_len = min([len(m) for m in mask_idcs])
518
+ for i, mask_idc in enumerate(mask_idcs):
519
+ if len(mask_idc) > min_len:
520
+ mask_idc = np.random.choice(mask_idc, min_len, replace=False)
521
+ mask[i, mask_idc] = True
522
+
523
+ return mask
524
+
525
+
526
+ def get_mem_usage():
527
+ try:
528
+ import psutil
529
+
530
+ mb = 1024 * 1024
531
+ return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
532
+ except ImportError:
533
+ return "N/A"
534
+
535
+
536
+ # lens: torch.LongTensor
537
+ # returns: torch.BoolTensor
538
+ def lengths_to_padding_mask(lens):
539
+ bsz, max_lens = lens.size(0), torch.max(lens).item()
540
+ mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
541
+ mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
542
+ return mask
543
+
544
+
545
+ # lens: torch.LongTensor
546
+ # returns: torch.BoolTensor
547
+ def lengths_to_mask(lens):
548
+ return ~lengths_to_padding_mask(lens)
549
+
550
+
551
+ def get_buckets(sizes, num_buckets):
552
+ buckets = np.unique(
553
+ np.percentile(
554
+ sizes,
555
+ np.linspace(0, 100, num_buckets + 1),
556
+ interpolation='lower',
557
+ )[1:]
558
+ )
559
+ return buckets
560
+
561
+
562
+ def get_bucketed_sizes(orig_sizes, buckets):
563
+ sizes = np.copy(orig_sizes)
564
+ assert np.min(sizes) >= 0
565
+ start_val = -1
566
+ for end_val in buckets:
567
+ mask = (sizes > start_val) & (sizes <= end_val)
568
+ sizes[mask] = end_val
569
+ start_val = end_val
570
+ return sizes
571
+
572
+
573
+
574
+ def _find_extra_valid_paths(dataset_path: str) -> set:
575
+ paths = utils.split_paths(dataset_path)
576
+ all_valid_paths = set()
577
+ for sub_dir in paths:
578
+ contents = PathManager.ls(sub_dir)
579
+ valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None]
580
+ all_valid_paths |= {os.path.basename(p) for p in valid_paths}
581
+ # Remove .bin, .idx etc
582
+ roots = {os.path.splitext(p)[0] for p in all_valid_paths}
583
+ return roots
584
+
585
+
586
+ def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
587
+ """Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
588
+ if (
589
+ train_cfg.dataset.ignore_unused_valid_subsets
590
+ or train_cfg.dataset.combine_valid_subsets
591
+ or train_cfg.dataset.disable_validation
592
+ or not hasattr(train_cfg.task, "data")
593
+ ):
594
+ return
595
+ other_paths = _find_extra_valid_paths(train_cfg.task.data)
596
+ specified_subsets = train_cfg.dataset.valid_subset.split(",")
597
+ ignored_paths = [p for p in other_paths if p not in specified_subsets]
598
+ if ignored_paths:
599
+ advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them."
600
+ msg = f"Valid paths {ignored_paths} will be ignored. {advice}"
601
+ raise ValueError(msg)
data/file_dataset.py ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import os
7
+ import torch
8
+ import pickle
9
+
10
+
11
+ class FileDataset:
12
+ def __init__(self, file_path, selected_col_ids=None, dtypes=None, separator="\t", cached_index=False):
13
+ self.file_path = file_path
14
+ assert os.path.exists(self.file_path), "Error: The local datafile {} not exists!".format(self.file_path)
15
+
16
+ self.separator = separator
17
+ if selected_col_ids is None:
18
+ # default to all fields
19
+ self.selected_col_ids = list(
20
+ range(len(open(self.file_path).readline().rstrip("\n").split(self.separator))))
21
+ else:
22
+ self.selected_col_ids = [int(col_id) for col_id in selected_col_ids.split(",")]
23
+ if dtypes is None:
24
+ # default to str
25
+ self.dtypes = [str for col_id in self.selected_col_ids]
26
+ else:
27
+ self.dtypes = [eval(col_dtype) for col_dtype in dtypes.split(",")]
28
+ assert len(self.dtypes) == len(self.selected_col_ids)
29
+
30
+ self.data_cnt = 0
31
+ try:
32
+ self.slice_id = torch.distributed.get_rank()
33
+ self.slice_count = torch.distributed.get_world_size()
34
+ except Exception:
35
+ self.slice_id = 0
36
+ self.slice_count = 1
37
+ self.cached_index = cached_index
38
+ self._init_seek_index()
39
+ self._reader = self._get_reader()
40
+ print("file {} slice_id {} row count {} total row count {}".format(
41
+ self.file_path, self.slice_id, self.row_count, self.total_row_count)
42
+ )
43
+
44
+ def _init_seek_index(self):
45
+ if self.cached_index:
46
+ cache_path = "{}.index".format(self.file_path)
47
+ assert os.path.exists(cache_path), "cache file {} not exists!".format(cache_path)
48
+ self.total_row_count, self.lineid_to_offset = pickle.load(open(cache_path, "rb"))
49
+ print("local datafile {} slice_id {} use cached row_count and line_idx-to-offset mapping".format(
50
+ self.file_path, self.slice_id))
51
+ else:
52
+ # make an iteration over the file to get row_count and line_idx-to-offset mapping
53
+ fp = open(self.file_path, "r")
54
+ print("local datafile {} slice_id {} begin to initialize row_count and line_idx-to-offset mapping".format(
55
+ self.file_path, self.slice_id))
56
+ self.total_row_count = 0
57
+ offset = 0
58
+ self.lineid_to_offset = []
59
+ for line in fp:
60
+ self.lineid_to_offset.append(offset)
61
+ self.total_row_count += 1
62
+ offset += len(line.encode('utf-8'))
63
+ self._compute_start_pos_and_row_count()
64
+ print("local datafile {} slice_id {} finished initializing row_count and line_idx-to-offset mapping".format(
65
+ self.file_path, self.slice_id))
66
+
67
+ def _compute_start_pos_and_row_count(self):
68
+ self.row_count = self.total_row_count // self.slice_count
69
+ if self.slice_id < self.total_row_count - self.row_count * self.slice_count:
70
+ self.row_count += 1
71
+ self.start_pos = self.row_count * self.slice_id
72
+ else:
73
+ self.start_pos = self.row_count * self.slice_id + (self.total_row_count - self.row_count * self.slice_count)
74
+
75
+ def _get_reader(self):
76
+ fp = open(self.file_path, "r")
77
+ fp.seek(self.lineid_to_offset[self.start_pos])
78
+ return fp
79
+
80
+ def _seek(self, offset=0):
81
+ try:
82
+ print("slice_id {} seek offset {}".format(self.slice_id, self.start_pos + offset))
83
+ self._reader.seek(self.lineid_to_offset[self.start_pos + offset])
84
+ self.data_cnt = offset
85
+ except Exception:
86
+ print("slice_id {} seek offset {}".format(self.slice_id, offset))
87
+ self._reader.seek(self.lineid_to_offset[offset])
88
+ self.data_cnt = offset
89
+
90
+ def __del__(self):
91
+ self._reader.close()
92
+
93
+ def __len__(self):
94
+ return self.row_count
95
+
96
+ def get_total_row_count(self):
97
+ return self.total_row_count
98
+
99
+ def __getitem__(self, index):
100
+ if self.data_cnt == self.row_count:
101
+ print("reach the end of datafile, start a new reader")
102
+ self.data_cnt = 0
103
+ self._reader = self._get_reader()
104
+ column_l = self._reader.readline().rstrip("\n").split(self.separator)
105
+ self.data_cnt += 1
106
+ column_l = [dtype(column_l[col_id]) for col_id, dtype in zip(self.selected_col_ids, self.dtypes)]
107
+ return column_l
data/mm_data/__init__.py ADDED
File without changes
data/mm_data/caption_dataset.py ADDED
@@ -0,0 +1,160 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import logging
9
+ import warnings
10
+ import string
11
+
12
+ import numpy as np
13
+ import torch
14
+ import base64
15
+ from torchvision import transforms
16
+
17
+ from PIL import Image, ImageFile
18
+
19
+ from data import data_utils
20
+ from data.ofa_dataset import OFADataset
21
+
22
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
23
+ ImageFile.MAX_IMAGE_PIXELS = None
24
+ Image.MAX_IMAGE_PIXELS = None
25
+
26
+ logger = logging.getLogger(__name__)
27
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
28
+
29
+ IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
30
+ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
31
+
32
+
33
+ def collate(samples, pad_idx, eos_idx):
34
+ if len(samples) == 0:
35
+ return {}
36
+
37
+ def merge(key):
38
+ return data_utils.collate_tokens(
39
+ [s[key] for s in samples],
40
+ pad_idx,
41
+ eos_idx=eos_idx,
42
+ )
43
+
44
+ id = np.array([s["id"] for s in samples])
45
+ src_tokens = merge("source")
46
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
47
+
48
+ patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
49
+ patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
50
+
51
+ prev_output_tokens = None
52
+ target = None
53
+ if samples[0].get("target", None) is not None:
54
+ target = merge("target")
55
+ tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
56
+ ntokens = tgt_lengths.sum().item()
57
+
58
+ if samples[0].get("prev_output_tokens", None) is not None:
59
+ prev_output_tokens = merge("prev_output_tokens")
60
+ else:
61
+ ntokens = src_lengths.sum().item()
62
+
63
+ batch = {
64
+ "id": id,
65
+ "nsentences": len(samples),
66
+ "ntokens": ntokens,
67
+ "net_input": {
68
+ "src_tokens": src_tokens,
69
+ "src_lengths": src_lengths,
70
+ "patch_images": patch_images,
71
+ "patch_masks": patch_masks,
72
+ "prev_output_tokens": prev_output_tokens
73
+ },
74
+ "target": target,
75
+ }
76
+
77
+ return batch
78
+
79
+
80
+ class CaptionDataset(OFADataset):
81
+ def __init__(
82
+ self,
83
+ split,
84
+ dataset,
85
+ bpe,
86
+ src_dict,
87
+ tgt_dict=None,
88
+ max_src_length=128,
89
+ max_tgt_length=30,
90
+ patch_image_size=224,
91
+ imagenet_default_mean_and_std=False,
92
+ scst=False
93
+ ):
94
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
95
+ self.max_src_length = max_src_length
96
+ self.max_tgt_length = max_tgt_length
97
+ self.patch_image_size = patch_image_size
98
+ self.scst = scst
99
+
100
+ self.transtab = str.maketrans({key: None for key in string.punctuation})
101
+
102
+ if imagenet_default_mean_and_std:
103
+ mean = IMAGENET_DEFAULT_MEAN
104
+ std = IMAGENET_DEFAULT_STD
105
+ else:
106
+ mean = [0.5, 0.5, 0.5]
107
+ std = [0.5, 0.5, 0.5]
108
+
109
+ self.patch_resize_transform = transforms.Compose([
110
+ lambda image: image.convert("RGB"),
111
+ transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
112
+ transforms.ToTensor(),
113
+ transforms.Normalize(mean=mean, std=std),
114
+ ])
115
+
116
+ if type(bpe).__name__ == 'GPT2BPE':
117
+ self.prompt = " what does the image describe?"
118
+ elif type(bpe).__name__ == 'BertBPE':
119
+ self.prompt = "图片描述了什么内容?"
120
+
121
+ def __getitem__(self, index):
122
+ uniq_id, image, caption = self.dataset[index]
123
+
124
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
125
+ patch_image = self.patch_resize_transform(image)
126
+ patch_mask = torch.tensor([True])
127
+
128
+ if self.split == 'train' and not self.scst:
129
+ caption = caption.translate(self.transtab).strip()
130
+ caption_token_list = caption.strip().split()
131
+ tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
132
+ else:
133
+ caption = ' '.join(caption.strip().split())
134
+ caption_list = [cap.translate(self.transtab).strip() for cap in caption.strip().split('&&')]
135
+ tgt_caption = '&&'.join(caption_list)
136
+ src_item = self.encode_text(self.prompt)
137
+ tgt_item = self.encode_text(" {}".format(tgt_caption))
138
+
139
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
140
+ target_item = torch.cat([tgt_item, self.eos_item])
141
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
142
+
143
+ example = {
144
+ "id": uniq_id,
145
+ "source": src_item,
146
+ "patch_image": patch_image,
147
+ "patch_mask": patch_mask,
148
+ "target": target_item,
149
+ "prev_output_tokens": prev_output_item
150
+ }
151
+ return example
152
+
153
+ def collater(self, samples, pad_to_length=None):
154
+ """Merge a list of samples to form a mini-batch.
155
+ Args:
156
+ samples (List[dict]): samples to collate
157
+ Returns:
158
+ dict: a mini-batch containing the data of the task
159
+ """
160
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/mm_data/image_gen_dataset.py ADDED
@@ -0,0 +1,171 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import logging
9
+ import warnings
10
+ import base64
11
+ import random
12
+
13
+ import numpy as np
14
+ import torch
15
+
16
+ from PIL import Image, ImageFile
17
+ from itertools import chain
18
+ from data.ofa_dataset import OFADataset
19
+ from data import data_utils
20
+
21
+ from PIL import Image
22
+ from io import BytesIO
23
+ import base64
24
+
25
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
26
+ ImageFile.MAX_IMAGE_PIXELS = None
27
+ Image.MAX_IMAGE_PIXELS = None
28
+
29
+ logger = logging.getLogger(__name__)
30
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
31
+
32
+
33
+ def collate(
34
+ samples,
35
+ pad_idx,
36
+ eos_idx,
37
+ left_pad_source=False,
38
+ left_pad_target=False,
39
+ ):
40
+ if len(samples) == 0:
41
+ return {}
42
+
43
+ def merge(key, left_pad, move_eos_to_beginning=False):
44
+ return data_utils.collate_tokens(
45
+ [s[key] for s in samples],
46
+ pad_idx,
47
+ eos_idx,
48
+ left_pad,
49
+ move_eos_to_beginning,
50
+ )
51
+
52
+ id = np.array([s["id"] for s in samples])
53
+ src_tokens = merge("source", left_pad=left_pad_source)
54
+ # sort by descending source length
55
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
56
+
57
+ code_images = np.array([s["code_image"] for s in samples])
58
+ code_masks = torch.cat([sample['code_mask'] for sample in samples])
59
+
60
+ prev_output_tokens = None
61
+ target = None
62
+ if samples[0].get("target", None) is not None:
63
+ target = merge("target", left_pad=left_pad_target)
64
+ tgt_lengths = torch.LongTensor(
65
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
66
+ )
67
+ ntokens = tgt_lengths.sum().item()
68
+
69
+ if samples[0].get("prev_output_tokens", None) is not None:
70
+ prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target)
71
+ else:
72
+ ntokens = src_lengths.sum().item()
73
+
74
+ batch = {
75
+ "id": id,
76
+ "nsentences": len(samples),
77
+ "ntokens": ntokens,
78
+ "net_input": {
79
+ "src_tokens": src_tokens,
80
+ "src_lengths": src_lengths,
81
+ "code_masks": code_masks,
82
+ "prev_output_tokens": prev_output_tokens
83
+ },
84
+ "code_images": code_images,
85
+ "target": target
86
+ }
87
+
88
+ return batch
89
+
90
+
91
+ def preprocess_vqgan(x):
92
+ x = 2. * x - 1.
93
+ return x
94
+
95
+
96
+ class ImageGenDataset(OFADataset):
97
+ def __init__(
98
+ self,
99
+ split,
100
+ dataset,
101
+ bpe,
102
+ src_dict,
103
+ tgt_dict=None,
104
+ max_src_length=128,
105
+ code_dict_size=8192,
106
+ code_image_size=256,
107
+ num_bins=1000
108
+ ):
109
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
110
+ self.max_src_length = max_src_length
111
+
112
+ self.code_dict_size = code_dict_size
113
+ self.num_codes = (code_image_size // 8) ** 2
114
+ self.num_bins = num_bins
115
+
116
+ slice_id = self.dataset.slice_id
117
+ empty_img = Image.new('RGB', (code_image_size, code_image_size))
118
+ empty_img.save(f'temp_{slice_id}.png')
119
+ img = Image.open(f'temp_{slice_id}.png')
120
+ img_buffer = BytesIO()
121
+ img.save(img_buffer, format=img.format)
122
+ byte_data = img_buffer.getvalue()
123
+ self.empty_image_base64 = base64.urlsafe_b64encode(byte_data)
124
+
125
+ def __getitem__(self, index):
126
+
127
+ data = self.dataset[index]
128
+ if len(data) == 2:
129
+ uniq_id, text = data
130
+ image_code = [0] * 1024
131
+ image = self.empty_image_base64
132
+ elif len(data) == 3:
133
+ uniq_id, text, image_code = data
134
+ image_code = [int(num) for num in image_code.strip().split()]
135
+ image = self.empty_image_base64
136
+ elif len(data) == 4:
137
+ uniq_id, image, text, image_code = data
138
+ image_code = [int(num) for num in image_code.strip().split()]
139
+ else:
140
+ raise NotImplementedError
141
+ code_mask = torch.tensor([True])
142
+ image_code = torch.LongTensor(image_code)
143
+ tgt_item = image_code + len(self.src_dict) - self.code_dict_size - self.num_bins
144
+ target_item = torch.cat([tgt_item, self.eos_item])
145
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
146
+
147
+ caption_token_list = text.strip().split()
148
+ caption = ' '.join(caption_token_list[:self.max_src_length])
149
+ src_item = self.encode_text(
150
+ " what is the complete image? caption: {}".format(caption),
151
+ append_bos=True,
152
+ append_eos=True
153
+ )
154
+ example = {
155
+ "id": uniq_id,
156
+ "source": src_item,
157
+ "code_mask": code_mask,
158
+ "code_image": image,
159
+ "target": target_item,
160
+ "prev_output_tokens": prev_output_item
161
+ }
162
+ return example
163
+
164
+ def collater(self, samples, pad_to_length=None):
165
+ """Merge a list of samples to form a mini-batch.
166
+ Args:
167
+ samples (List[dict]): samples to collate
168
+ Returns:
169
+ dict: a mini-batch containing the data of the task
170
+ """
171
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/mm_data/ocr_dataset.py ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import logging
9
+ import warnings
10
+ import random
11
+ import functools
12
+
13
+ import torch
14
+ import base64
15
+ from torchvision import transforms
16
+ from torchvision.transforms import InterpolationMode
17
+ from torchvision.transforms import functional as F
18
+
19
+ from PIL import Image, ImageFile
20
+
21
+ from zhconv import convert
22
+ import unicodedata
23
+
24
+ from data import data_utils
25
+ from data.ofa_dataset import OFADataset
26
+
27
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
28
+ ImageFile.MAX_IMAGE_PIXELS = None
29
+ Image.MAX_IMAGE_PIXELS = None
30
+
31
+ logger = logging.getLogger(__name__)
32
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
33
+
34
+ IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
35
+ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
36
+
37
+
38
+ def collate(samples, pad_idx, eos_idx):
39
+ if len(samples) == 0:
40
+ return {}
41
+
42
+ def merge(key):
43
+ return data_utils.collate_tokens(
44
+ [s[key] for s in samples],
45
+ pad_idx,
46
+ eos_idx=eos_idx,
47
+ )
48
+
49
+ id = np.array([s["id"] for s in samples])
50
+ src_tokens = merge("source")
51
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
52
+
53
+ patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
54
+ patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
55
+
56
+ prev_output_tokens = None
57
+ target = None
58
+ if samples[0].get("target", None) is not None:
59
+ target = merge("target")
60
+ tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
61
+ ntokens = tgt_lengths.sum().item()
62
+
63
+ if samples[0].get("prev_output_tokens", None) is not None:
64
+ prev_output_tokens = merge("prev_output_tokens")
65
+ else:
66
+ ntokens = src_lengths.sum().item()
67
+
68
+ batch = {
69
+ "id": id,
70
+ "nsentences": len(samples),
71
+ "ntokens": ntokens,
72
+ "net_input": {
73
+ "src_tokens": src_tokens,
74
+ "src_lengths": src_lengths,
75
+ "patch_images": patch_images,
76
+ "patch_masks": patch_masks,
77
+ "prev_output_tokens": prev_output_tokens
78
+ },
79
+ "target": target,
80
+ }
81
+
82
+ return batch
83
+
84
+
85
+ def ocr_resize(img, patch_image_size, is_document=False):
86
+ img = img.convert("RGB")
87
+ width, height = img.size
88
+
89
+ if is_document:
90
+ new_height, new_width = 64, 1920
91
+ else:
92
+ if width >= height:
93
+ new_width = max(64, patch_image_size)
94
+ new_height = max(64, int(patch_image_size * (height / width)))
95
+ top = random.randint(0, patch_image_size - new_height)
96
+ bottom = patch_image_size - new_height - top
97
+ left, right = 0, 0
98
+ else:
99
+ new_height = max(64, patch_image_size)
100
+ new_width = max(64, int(patch_image_size * (width / height)))
101
+ left = random.randint(0, patch_image_size - new_width)
102
+ right = patch_image_size - new_width - left
103
+ top, bottom = 0, 0
104
+
105
+ img_new = F.resize(
106
+ img,
107
+ [new_height, new_width],
108
+ interpolation=InterpolationMode.BICUBIC,
109
+ )
110
+
111
+ if is_document:
112
+ img_split = transforms.ToTensor()(img_new).chunk(4, dim=-1)
113
+ img_new = transforms.ToPILImage()(torch.cat(img_split, dim=-2))
114
+ new_width, new_height = img_new.size
115
+ top = random.randint(0, patch_image_size - new_height)
116
+ bottom = patch_image_size - new_height - top
117
+ left, right = 0, 0
118
+
119
+ img_new = F.pad(img_new, padding=[left, top, right, bottom], padding_mode="edge")
120
+ assert img_new.size == (patch_image_size, patch_image_size)
121
+
122
+ return img_new
123
+
124
+
125
+ class OcrDataset(OFADataset):
126
+ def __init__(
127
+ self,
128
+ split,
129
+ dataset,
130
+ bpe,
131
+ src_dict,
132
+ tgt_dict=None,
133
+ max_src_length=80,
134
+ max_tgt_length=30,
135
+ patch_image_size=224,
136
+ imagenet_default_mean_and_std=False,
137
+ is_document=False,
138
+ ):
139
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
140
+ self.max_src_length = max_src_length
141
+ self.max_tgt_length = max_tgt_length
142
+ self.patch_image_size = patch_image_size
143
+
144
+ if imagenet_default_mean_and_std:
145
+ mean = IMAGENET_DEFAULT_MEAN
146
+ std = IMAGENET_DEFAULT_STD
147
+ else:
148
+ mean = [0.5, 0.5, 0.5]
149
+ std = [0.5, 0.5, 0.5]
150
+
151
+ self.patch_resize_transform = transforms.Compose(
152
+ [
153
+ lambda image: ocr_resize(
154
+ image, patch_image_size, is_document=is_document
155
+ ),
156
+ transforms.ToTensor(),
157
+ transforms.Normalize(mean=mean, std=std),
158
+ ]
159
+ )
160
+
161
+ self.bpe = bpe
162
+ if type(bpe).__name__ == 'GPT2BPE':
163
+ self.prompt = " what are the texts on the image?"
164
+ elif type(bpe).__name__ == 'BertBPE':
165
+ self.prompt = "图片上的文字是什么?"
166
+
167
+ def __getitem__(self, index):
168
+ uniq_id, image, caption = self.dataset[index]
169
+
170
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
171
+ patch_image = self.patch_resize_transform(image)
172
+ patch_mask = torch.tensor([True])
173
+
174
+ caption = unicodedata.normalize("NFKC", convert(caption, "zh-hans"))
175
+ if type(self.bpe).__name__ == 'GPT2BPE':
176
+ caption_token_list = caption.lower().strip().split()
177
+ tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
178
+ elif type(self.bpe).__name__ == 'BertBPE':
179
+ tgt_caption = caption[: self.max_tgt_length].lower()
180
+ src_item = self.encode_text(self.prompt)
181
+ tgt_item = self.encode_text(" {}".format(tgt_caption))
182
+
183
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
184
+ target_item = torch.cat([tgt_item, self.eos_item])
185
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
186
+
187
+ example = {
188
+ "id": uniq_id,
189
+ "source": src_item,
190
+ "patch_image": patch_image,
191
+ "patch_mask": patch_mask,
192
+ "target": target_item,
193
+ "prev_output_tokens": prev_output_item,
194
+ }
195
+ return example
196
+
197
+ def collater(self, samples, pad_to_length=None):
198
+ """Merge a list of samples to form a mini-batch.
199
+ Args:
200
+ samples (List[dict]): samples to collate
201
+ Returns:
202
+ dict: a mini-batch containing the data required for the task
203
+ """
204
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/mm_data/refcoco_dataset.py ADDED
@@ -0,0 +1,174 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import logging
9
+ import warnings
10
+
11
+ import numpy as np
12
+ import torch
13
+ import base64
14
+ import utils.transforms as T
15
+
16
+ from PIL import Image, ImageFile
17
+
18
+ from data import data_utils
19
+ from data.ofa_dataset import OFADataset
20
+
21
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
22
+ ImageFile.MAX_IMAGE_PIXELS = None
23
+ Image.MAX_IMAGE_PIXELS = None
24
+
25
+ logger = logging.getLogger(__name__)
26
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
27
+
28
+ IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
29
+ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
30
+
31
+
32
+ def collate(samples, pad_idx, eos_idx):
33
+ if len(samples) == 0:
34
+ return {}
35
+
36
+ def merge(key):
37
+ return data_utils.collate_tokens(
38
+ [s[key] for s in samples],
39
+ pad_idx,
40
+ eos_idx=eos_idx,
41
+ )
42
+
43
+ id = np.array([s["id"] for s in samples])
44
+ src_tokens = merge("source")
45
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
46
+
47
+ patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
48
+ patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
49
+
50
+ w_resize_ratios = torch.stack([s["w_resize_ratio"] for s in samples], dim=0)
51
+ h_resize_ratios = torch.stack([s["h_resize_ratio"] for s in samples], dim=0)
52
+ region_coords = torch.stack([s['region_coord'] for s in samples], dim=0)
53
+
54
+ prev_output_tokens = None
55
+ target = None
56
+ if samples[0].get("target", None) is not None:
57
+ target = merge("target")
58
+ tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
59
+ ntokens = tgt_lengths.sum().item()
60
+
61
+ if samples[0].get("prev_output_tokens", None) is not None:
62
+ prev_output_tokens = merge("prev_output_tokens")
63
+ else:
64
+ ntokens = src_lengths.sum().item()
65
+
66
+ batch = {
67
+ "id": id,
68
+ "nsentences": len(samples),
69
+ "ntokens": ntokens,
70
+ "net_input": {
71
+ "src_tokens": src_tokens,
72
+ "src_lengths": src_lengths,
73
+ "patch_images": patch_images,
74
+ "patch_masks": patch_masks,
75
+ "prev_output_tokens": prev_output_tokens
76
+ },
77
+ "target": target,
78
+ "w_resize_ratios": w_resize_ratios,
79
+ "h_resize_ratios": h_resize_ratios,
80
+ "region_coords": region_coords
81
+ }
82
+
83
+ return batch
84
+
85
+
86
+ class RefcocoDataset(OFADataset):
87
+ def __init__(
88
+ self,
89
+ split,
90
+ dataset,
91
+ bpe,
92
+ src_dict,
93
+ tgt_dict=None,
94
+ max_src_length=80,
95
+ max_tgt_length=30,
96
+ patch_image_size=512,
97
+ imagenet_default_mean_and_std=False,
98
+ num_bins=1000,
99
+ max_image_size=512
100
+ ):
101
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
102
+ self.max_src_length = max_src_length
103
+ self.max_tgt_length = max_tgt_length
104
+ self.patch_image_size = patch_image_size
105
+ self.num_bins = num_bins
106
+
107
+ if imagenet_default_mean_and_std:
108
+ mean = IMAGENET_DEFAULT_MEAN
109
+ std = IMAGENET_DEFAULT_STD
110
+ else:
111
+ mean = [0.5, 0.5, 0.5]
112
+ std = [0.5, 0.5, 0.5]
113
+
114
+ # for positioning
115
+ self.positioning_transform = T.Compose([
116
+ T.RandomResize([patch_image_size], max_size=patch_image_size),
117
+ T.ToTensor(),
118
+ T.Normalize(mean=mean, std=std, max_image_size=max_image_size)
119
+ ])
120
+
121
+ if type(bpe).__name__ == 'GPT2BPE':
122
+ self.prompt = ' which region does the text " {} " describe?'
123
+ elif type(bpe).__name__ == 'BertBPE':
124
+ self.prompt = '这段文字" {} "描述的是哪个区域?'
125
+
126
+ def __getitem__(self, index):
127
+ uniq_id, base64_str, text, region_coord = self.dataset[index]
128
+
129
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(base64_str))).convert("RGB")
130
+ w, h = image.size
131
+ boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
132
+ x0, y0, x1, y1 = region_coord.strip().split(',')
133
+ region = torch.tensor([float(x0), float(y0), float(x1), float(y1)])
134
+ boxes_target["boxes"] = torch.tensor([[float(x0), float(y0), float(x1), float(y1)]])
135
+ boxes_target["labels"] = np.array([0])
136
+ boxes_target["area"] = torch.tensor([(float(x1) - float(x0)) * (float(y1) - float(y0))])
137
+
138
+ patch_image, patch_boxes = self.positioning_transform(image, boxes_target)
139
+ resize_h, resize_w = patch_boxes["size"][0], patch_boxes["size"][1]
140
+ patch_mask = torch.tensor([True])
141
+ quant_x0 = "<bin_{}>".format(int((patch_boxes["boxes"][0][0] * (self.num_bins - 1)).round()))
142
+ quant_y0 = "<bin_{}>".format(int((patch_boxes["boxes"][0][1] * (self.num_bins - 1)).round()))
143
+ quant_x1 = "<bin_{}>".format(int((patch_boxes["boxes"][0][2] * (self.num_bins - 1)).round()))
144
+ quant_y1 = "<bin_{}>".format(int((patch_boxes["boxes"][0][3] * (self.num_bins - 1)).round()))
145
+ region_coord = "{} {} {} {}".format(quant_x0, quant_y0, quant_x1, quant_y1)
146
+ src_caption = self.pre_caption(text, self.max_src_length)
147
+ src_item = self.encode_text(self.prompt.format(src_caption))
148
+ tgt_item = self.encode_text(region_coord, use_bpe=False)
149
+
150
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
151
+ target_item = torch.cat([tgt_item, self.eos_item])
152
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
153
+
154
+ example = {
155
+ "id": uniq_id,
156
+ "source": src_item,
157
+ "patch_image": patch_image,
158
+ "patch_mask": patch_mask,
159
+ "target": target_item,
160
+ "prev_output_tokens": prev_output_item,
161
+ "w_resize_ratio": resize_w / w,
162
+ "h_resize_ratio": resize_h / h,
163
+ "region_coord": region
164
+ }
165
+ return example
166
+
167
+ def collater(self, samples, pad_to_length=None):
168
+ """Merge a list of samples to form a mini-batch.
169
+ Args:
170
+ samples (List[dict]): samples to collate
171
+ Returns:
172
+ dict: a mini-batch containing the data of the task
173
+ """
174
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/mm_data/snli_ve_dataset.py ADDED
@@ -0,0 +1,203 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import logging
9
+ import warnings
10
+
11
+ import numpy as np
12
+ import torch
13
+ import base64
14
+ from torchvision import transforms
15
+
16
+ from PIL import Image, ImageFile
17
+
18
+ from data import data_utils
19
+ from data.ofa_dataset import OFADataset
20
+
21
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
22
+ ImageFile.MAX_IMAGE_PIXELS = None
23
+ Image.MAX_IMAGE_PIXELS = None
24
+
25
+ logger = logging.getLogger(__name__)
26
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
27
+
28
+ IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
29
+ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
30
+
31
+
32
+ def collate(samples, pad_idx, eos_idx):
33
+ if len(samples) == 0:
34
+ return {}
35
+
36
+ def merge(key):
37
+ return data_utils.collate_tokens(
38
+ [s[key] for s in samples],
39
+ pad_idx,
40
+ eos_idx=eos_idx,
41
+ )
42
+
43
+ id = np.array([s["id"] for s in samples])
44
+ src_tokens = merge("source")
45
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
46
+
47
+ patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
48
+ patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
49
+
50
+ ref_dict = None
51
+ if samples[0].get("ref_dict", None) is not None:
52
+ ref_dict = np.array([s['ref_dict'] for s in samples])
53
+
54
+ constraint_masks = None
55
+ if samples[0].get("constraint_mask", None) is not None:
56
+ constraint_masks = merge("constraint_mask")
57
+
58
+ decoder_prompts = None
59
+ if samples[0].get("decoder_prompt", None) is not None:
60
+ decoder_prompts = np.array([s['decoder_prompt'].tolist() for s in samples])
61
+
62
+ prev_output_tokens = None
63
+ target = None
64
+ if samples[0].get("target", None) is not None:
65
+ target = merge("target")
66
+ tgt_lengths = torch.LongTensor(
67
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
68
+ )
69
+ ntokens = tgt_lengths.sum().item()
70
+
71
+ if samples[0].get("prev_output_tokens", None) is not None:
72
+ prev_output_tokens = merge("prev_output_tokens")
73
+ else:
74
+ ntokens = src_lengths.sum().item()
75
+
76
+ batch = {
77
+ "id": id,
78
+ "nsentences": len(samples),
79
+ "ntokens": ntokens,
80
+ "net_input": {
81
+ "src_tokens": src_tokens,
82
+ "src_lengths": src_lengths,
83
+ "patch_images": patch_images,
84
+ "patch_masks": patch_masks,
85
+ "prev_output_tokens": prev_output_tokens
86
+ },
87
+ "ref_dict": ref_dict,
88
+ "constraint_masks": constraint_masks,
89
+ "decoder_prompts": decoder_prompts,
90
+ "target": target
91
+ }
92
+
93
+ return batch
94
+
95
+
96
+ class SnliVeDataset(OFADataset):
97
+ def __init__(
98
+ self,
99
+ split,
100
+ dataset,
101
+ bpe,
102
+ src_dict,
103
+ tgt_dict=None,
104
+ max_src_length=80,
105
+ max_tgt_length=30,
106
+ patch_image_size=224,
107
+ add_caption=False,
108
+ constraint_trie=None,
109
+ imagenet_default_mean_and_std=False,
110
+ prompt_type="none"
111
+ ):
112
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
113
+ self.max_src_length = max_src_length
114
+ self.max_tgt_length = max_tgt_length
115
+ self.patch_image_size = patch_image_size
116
+
117
+ self.add_caption = add_caption
118
+ self.constraint_trie = constraint_trie
119
+ self.prompt_type = prompt_type
120
+
121
+ if imagenet_default_mean_and_std:
122
+ mean = IMAGENET_DEFAULT_MEAN
123
+ std = IMAGENET_DEFAULT_STD
124
+ else:
125
+ mean = [0.5, 0.5, 0.5]
126
+ std = [0.5, 0.5, 0.5]
127
+
128
+ self.patch_resize_transform = transforms.Compose([
129
+ lambda image: image.convert("RGB"),
130
+ transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
131
+ transforms.ToTensor(),
132
+ transforms.Normalize(mean=mean, std=std),
133
+ ])
134
+
135
+ def __getitem__(self, index):
136
+ uniq_id, image, hypothesis, caption, label = self.dataset[index]
137
+ if label == 'contradiction':
138
+ label = 'no'
139
+ elif label == 'entailment':
140
+ label = 'yes'
141
+ elif label == 'neutral':
142
+ label = 'maybe'
143
+ else:
144
+ raise NotImplementedError
145
+
146
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
147
+ patch_image = self.patch_resize_transform(image)
148
+ patch_mask = torch.tensor([True])
149
+
150
+ hypothesis = self.pre_caption(hypothesis, self.max_src_length)
151
+ src_item = self.encode_text(' does the image describe " {} "?'.format(hypothesis))
152
+ tgt_item = self.encode_text(" {}".format(label))
153
+ ref_dict = {label: 1.0}
154
+
155
+ if self.add_caption:
156
+ caption = self.pre_caption(caption, self.max_src_length)
157
+ src_item = self.encode_text(' can image and text1 " {} " imply text2 " {} "?'.format(caption, hypothesis))
158
+
159
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
160
+ if self.prompt_type == 'none':
161
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
162
+ target_item = torch.cat([prev_output_item[1:], self.eos_item])
163
+ decoder_prompt = self.bos_item
164
+ elif self.prompt_type == 'src':
165
+ prev_output_item = torch.cat([src_item, tgt_item])
166
+ target_item = torch.cat([prev_output_item[1:], self.eos_item])
167
+ decoder_prompt = src_item
168
+ elif self.prompt_type == 'prev_output':
169
+ prev_output_item = torch.cat([src_item[:-1], tgt_item])
170
+ target_item = torch.cat([prev_output_item[1:], self.eos_item])
171
+ decoder_prompt = src_item[:-1]
172
+ else:
173
+ raise NotImplementedError
174
+ target_item[:-len(tgt_item)-1] = self.tgt_dict.pad()
175
+
176
+ example = {
177
+ "id": uniq_id,
178
+ "source": src_item,
179
+ "patch_image": patch_image,
180
+ "patch_mask": patch_mask,
181
+ "target": target_item,
182
+ "prev_output_tokens": prev_output_item,
183
+ "decoder_prompt": decoder_prompt,
184
+ "ref_dict": ref_dict,
185
+ }
186
+ if self.constraint_trie is not None:
187
+ constraint_mask = torch.zeros((len(target_item), len(self.tgt_dict))).bool()
188
+ start_idx = len(target_item) - len(tgt_item) - 1
189
+ for i in range(len(target_item)-len(tgt_item)-1, len(target_item)):
190
+ constraint_prefix_token = [self.tgt_dict.bos()] + target_item[start_idx:i].tolist()
191
+ constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
192
+ constraint_mask[i][constraint_nodes] = True
193
+ example["constraint_mask"] = constraint_mask
194
+ return example
195
+
196
+ def collater(self, samples, pad_to_length=None):
197
+ """Merge a list of samples to form a mini-batch.
198
+ Args:
199
+ samples (List[dict]): samples to collate
200
+ Returns:
201
+ dict: a mini-batch containing the data of the task
202
+ """
203
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/mm_data/vqa_gen_dataset.py ADDED
@@ -0,0 +1,218 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import logging
9
+ import warnings
10
+
11
+ import numpy as np
12
+ import torch
13
+ import base64
14
+ from torchvision import transforms
15
+
16
+ from PIL import Image, ImageFile
17
+
18
+ from data import data_utils
19
+ from data.ofa_dataset import OFADataset
20
+
21
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
22
+ ImageFile.MAX_IMAGE_PIXELS = None
23
+ Image.MAX_IMAGE_PIXELS = None
24
+
25
+ logger = logging.getLogger(__name__)
26
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
27
+
28
+ IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
29
+ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
30
+
31
+
32
+ def collate(samples, pad_idx, eos_idx):
33
+ if len(samples) == 0:
34
+ return {}
35
+
36
+ def merge(key):
37
+ return data_utils.collate_tokens(
38
+ [s[key] for s in samples],
39
+ pad_idx,
40
+ eos_idx=eos_idx,
41
+ )
42
+
43
+ id = np.array([s["id"] for s in samples])
44
+ src_tokens = merge("source")
45
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
46
+
47
+ patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
48
+ patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
49
+
50
+ conf = None
51
+ if samples[0].get("conf", None) is not None:
52
+ conf = torch.cat([s['conf'] for s in samples], dim=0)
53
+
54
+ ref_dict = None
55
+ if samples[0].get("ref_dict", None) is not None:
56
+ ref_dict = np.array([s['ref_dict'] for s in samples])
57
+
58
+ constraint_masks = None
59
+ if samples[0].get("constraint_mask", None) is not None:
60
+ constraint_masks = merge("constraint_mask")
61
+
62
+ decoder_prompts = None
63
+ if samples[0].get("decoder_prompt", None) is not None:
64
+ decoder_prompts = np.array([s['decoder_prompt'].tolist() for s in samples])
65
+
66
+ prefix_tokens = None
67
+ if samples[0].get("decoder_prompt", None) is not None:
68
+ prefix_tokens = merge("decoder_prompt")
69
+ prefix_tokens = prefix_tokens[:, 1:]
70
+
71
+ prev_output_tokens = None
72
+ target = None
73
+ if samples[0].get("target", None) is not None:
74
+ target = merge("target")
75
+ tgt_lengths = torch.LongTensor(
76
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
77
+ )
78
+ ntokens = tgt_lengths.sum().item()
79
+
80
+ if samples[0].get("prev_output_tokens", None) is not None:
81
+ prev_output_tokens = merge("prev_output_tokens")
82
+ else:
83
+ ntokens = src_lengths.sum().item()
84
+
85
+ batch = {
86
+ "id": id,
87
+ "nsentences": len(samples),
88
+ "ntokens": ntokens,
89
+ "net_input": {
90
+ "src_tokens": src_tokens,
91
+ "src_lengths": src_lengths,
92
+ "patch_images": patch_images,
93
+ "patch_masks": patch_masks,
94
+ "prev_output_tokens": prev_output_tokens
95
+ },
96
+ "conf": conf,
97
+ "ref_dict": ref_dict,
98
+ "constraint_masks": constraint_masks,
99
+ "decoder_prompts": decoder_prompts,
100
+ "target": target,
101
+ "prefix_tokens": prefix_tokens
102
+ }
103
+
104
+ return batch
105
+
106
+
107
+ class VqaGenDataset(OFADataset):
108
+ def __init__(
109
+ self,
110
+ split,
111
+ dataset,
112
+ bpe,
113
+ src_dict,
114
+ tgt_dict=None,
115
+ max_src_length=128,
116
+ max_object_length=30,
117
+ max_tgt_length=30,
118
+ patch_image_size=224,
119
+ add_object=False,
120
+ constraint_trie=None,
121
+ imagenet_default_mean_and_std=False,
122
+ prompt_type="none"
123
+ ):
124
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
125
+ self.max_src_length = max_src_length
126
+ self.max_object_length = max_object_length
127
+ self.max_tgt_length = max_tgt_length
128
+ self.patch_image_size = patch_image_size
129
+
130
+ self.add_object = add_object
131
+ self.constraint_trie = constraint_trie
132
+ self.prompt_type = prompt_type
133
+
134
+ if imagenet_default_mean_and_std:
135
+ mean = IMAGENET_DEFAULT_MEAN
136
+ std = IMAGENET_DEFAULT_STD
137
+ else:
138
+ mean = [0.5, 0.5, 0.5]
139
+ std = [0.5, 0.5, 0.5]
140
+
141
+ self.patch_resize_transform = transforms.Compose([
142
+ lambda image: image.convert("RGB"),
143
+ transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
144
+ transforms.ToTensor(),
145
+ transforms.Normalize(mean=mean, std=std),
146
+ ])
147
+
148
+ def __getitem__(self, index):
149
+ item = self.dataset[index]
150
+ if len(item) == 5:
151
+ uniq_id, image, question, ref, predict_objects = item
152
+ else:
153
+ uniq_id, image, question, ref, predict_objects, caption = item
154
+
155
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
156
+ patch_image = self.patch_resize_transform(image)
157
+ patch_mask = torch.tensor([True])
158
+
159
+ question = self.pre_question(question, self.max_src_length)
160
+ question = question + '?' if not question.endswith('?') else question
161
+ src_item = self.encode_text(' {}'.format(question))
162
+
163
+ ref_dict = {item.split('|!+')[1]: float(item.split('|!+')[0]) for item in ref.split('&&')}
164
+ answer = max(ref_dict, key=ref_dict.get)
165
+ conf = torch.tensor([ref_dict[answer]])
166
+ tgt_item = self.encode_text(" {}".format(answer))
167
+
168
+ if self.add_object and predict_objects is not None:
169
+ predict_object_seq = ' '.join(predict_objects.strip().split('&&')[:self.max_object_length])
170
+ predict_object_item = self.encode_text(" object: {}".format(predict_object_seq))
171
+ src_item = torch.cat([src_item, predict_object_item])
172
+
173
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
174
+ if self.prompt_type == 'none':
175
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
176
+ target_item = torch.cat([prev_output_item[1:], self.eos_item])
177
+ decoder_prompt = self.bos_item
178
+ elif self.prompt_type == 'src':
179
+ prev_output_item = torch.cat([src_item, tgt_item])
180
+ target_item = torch.cat([prev_output_item[1:], self.eos_item])
181
+ decoder_prompt = src_item
182
+ elif self.prompt_type == 'prev_output':
183
+ prev_output_item = torch.cat([src_item[:-1], tgt_item])
184
+ target_item = torch.cat([prev_output_item[1:], self.eos_item])
185
+ decoder_prompt = src_item[:-1]
186
+ else:
187
+ raise NotImplementedError
188
+ target_item[:-len(tgt_item)-1] = self.tgt_dict.pad()
189
+
190
+ example = {
191
+ "id": uniq_id,
192
+ "source": src_item,
193
+ "patch_image": patch_image,
194
+ "patch_mask": patch_mask,
195
+ "target": target_item,
196
+ "prev_output_tokens": prev_output_item,
197
+ "decoder_prompt": decoder_prompt,
198
+ "ref_dict": ref_dict,
199
+ "conf": conf,
200
+ }
201
+ if self.constraint_trie is not None:
202
+ constraint_mask = torch.zeros((len(target_item), len(self.tgt_dict))).bool()
203
+ start_idx = len(target_item) - len(tgt_item) - 1
204
+ for i in range(len(target_item)-len(tgt_item)-1, len(target_item)):
205
+ constraint_prefix_token = [self.tgt_dict.bos()] + target_item[start_idx:i].tolist()
206
+ constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
207
+ constraint_mask[i][constraint_nodes] = True
208
+ example["constraint_mask"] = constraint_mask
209
+ return example
210
+
211
+ def collater(self, samples, pad_to_length=None):
212
+ """Merge a list of samples to form a mini-batch.
213
+ Args:
214
+ samples (List[dict]): samples to collate
215
+ Returns:
216
+ dict: a mini-batch containing the data of the task
217
+ """
218
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlg_data/summary_dataset.py ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ prev_output_tokens = None
33
+ target = None
34
+ if samples[0].get("target", None) is not None:
35
+ target = merge("target")
36
+ tgt_lengths = torch.LongTensor(
37
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
38
+ )
39
+ ntokens = tgt_lengths.sum().item()
40
+
41
+ if samples[0].get("prev_output_tokens", None) is not None:
42
+ prev_output_tokens = merge("prev_output_tokens")
43
+ else:
44
+ ntokens = src_lengths.sum().item()
45
+
46
+ target_strs = np.array([s["target_str"] for s in samples])
47
+
48
+ batch = {
49
+ "nsentences": len(samples),
50
+ "ntokens": ntokens,
51
+ "net_input": {
52
+ "src_tokens": src_tokens,
53
+ "src_lengths": src_lengths,
54
+ "prev_output_tokens": prev_output_tokens
55
+ },
56
+ "target": target,
57
+ "target_strs": target_strs
58
+ }
59
+
60
+ return batch
61
+
62
+
63
+ class SummaryDataset(OFADataset):
64
+ def __init__(
65
+ self,
66
+ split,
67
+ dataset,
68
+ bpe,
69
+ src_dict,
70
+ tgt_dict=None,
71
+ code_dict_size=8192,
72
+ num_bins=1000,
73
+ max_src_length=512,
74
+ max_tgt_length=128,
75
+ noise_ratio=0.0
76
+ ):
77
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
78
+ self.max_src_length = max_src_length
79
+ self.max_tgt_length = max_tgt_length
80
+ self.code_dict_size = code_dict_size
81
+ self.num_bins = num_bins
82
+ self.noise_ratio = noise_ratio
83
+
84
+ if type(bpe).__name__ == 'GPT2BPE':
85
+ self.prompt = ' what is the summary of article " {} "?'
86
+ elif type(bpe).__name__ == 'BertBPE':
87
+ self.prompt = "{} 请用一个句子简单总结上文:"
88
+
89
+ def __getitem__(self, index):
90
+ source, target = self.dataset[index]
91
+ target_str = target.lower()
92
+
93
+ source = self.pre_caption(source, max_words=self.max_src_length)
94
+ target = self.pre_caption(target, max_words=self.max_tgt_length)
95
+ source = source.replace('<unk>', 'unk')
96
+ target = target.replace('<unk>', 'unk')
97
+
98
+ src_item = self.encode_text(
99
+ self.prompt.format(source),
100
+ length=self.max_src_length
101
+ )
102
+ tgt_item = self.encode_text('{}'.format(target))
103
+ noise_tgt_item = self.add_noise_to_tgt(tgt_item.clone(), self.noise_ratio)
104
+
105
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
106
+ target_item = torch.cat([tgt_item, self.eos_item])
107
+ prev_output_item = torch.cat([self.bos_item, noise_tgt_item])
108
+
109
+ example = {
110
+ "source": src_item,
111
+ "target": target_item,
112
+ "prev_output_tokens": prev_output_item,
113
+ "target_str": target_str
114
+ }
115
+ return example
116
+
117
+ def add_noise_to_tgt(self, target, p):
118
+ noise_indices = torch.FloatTensor(target.size(0)).uniform_() < p
119
+ target[noise_indices] = torch.randint(
120
+ 4, len(self.src_dict) - self.code_dict_size - self.num_bins, size=(noise_indices.sum(),)
121
+ )
122
+ return target
123
+
124
+ def collater(self, samples, pad_to_length=None):
125
+ """Merge a list of samples to form a mini-batch.
126
+ Args:
127
+ samples (List[dict]): samples to collate
128
+ Returns:
129
+ dict: a mini-batch containing the data of the task
130
+ """
131
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlu_data/cola_dataset.py ADDED
@@ -0,0 +1,138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ ref_dict = None
33
+ if samples[0].get("ref_dict", None) is not None:
34
+ ref_dict = np.array([s['ref_dict'] for s in samples])
35
+
36
+ constraint_masks = None
37
+ if samples[0].get("constraint_mask", None) is not None:
38
+ constraint_masks = merge("constraint_mask")
39
+
40
+ prev_output_tokens = None
41
+ target = None
42
+ if samples[0].get("target", None) is not None:
43
+ target = merge("target")
44
+ tgt_lengths = torch.LongTensor(
45
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
46
+ )
47
+ ntokens = tgt_lengths.sum().item()
48
+
49
+ if samples[0].get("prev_output_tokens", None) is not None:
50
+ prev_output_tokens = merge("prev_output_tokens")
51
+ else:
52
+ ntokens = src_lengths.sum().item()
53
+
54
+ batch = {
55
+ "nsentences": len(samples),
56
+ "ntokens": ntokens,
57
+ "net_input": {
58
+ "src_tokens": src_tokens,
59
+ "src_lengths": src_lengths,
60
+ "prev_output_tokens": prev_output_tokens
61
+ },
62
+ "ref_dict": ref_dict,
63
+ "constraint_masks": constraint_masks,
64
+ "target": target,
65
+ }
66
+
67
+ return batch
68
+
69
+
70
+ class COLADataset(OFADataset):
71
+ def __init__(
72
+ self,
73
+ split,
74
+ dataset,
75
+ bpe,
76
+ src_dict,
77
+ tgt_dict=None,
78
+ max_src_length=512,
79
+ max_tgt_length=30,
80
+ constraint_trie=None,
81
+ prompt_type="none"
82
+ ):
83
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
84
+ self.max_src_length = max_src_length
85
+ self.max_tgt_length = max_tgt_length
86
+ self.constraint_trie = constraint_trie
87
+ self.prompt_type = prompt_type
88
+
89
+ def __getitem__(self, index):
90
+ sentence, label = self.dataset[index]
91
+ if label == '0':
92
+ label = 'no'
93
+ elif label == '1':
94
+ label = 'yes'
95
+ else:
96
+ raise NotImplementedError
97
+
98
+ sentence = ' '.join(sentence.lower().strip().split()[:self.max_src_length])
99
+ src_item = self.encode_text(' is the text " {} " grammatically correct?'.format(sentence))
100
+ tgt_item = self.encode_text(" {}".format(label))
101
+ assert tgt_item.size(0) == 1
102
+ ref_dict = {label: 1.0}
103
+
104
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
105
+ if self.prompt_type == 'none':
106
+ prev_output_item = self.bos_item
107
+ target_item = tgt_item
108
+ elif self.prompt_type == 'src':
109
+ prev_output_item = src_item.clone()
110
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
111
+ elif self.prompt_type == 'prev_output':
112
+ prev_output_item = src_item[:-1].clone()
113
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
114
+ else:
115
+ raise NotImplementedError
116
+ target_item[:-1] = self.tgt_dict.pad()
117
+
118
+ example = {
119
+ "source": src_item,
120
+ "target": target_item,
121
+ "prev_output_tokens": prev_output_item,
122
+ "ref_dict": ref_dict,
123
+ }
124
+ if self.constraint_trie is not None:
125
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
126
+ constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
127
+ constraint_mask[-1][constraint_nodes] = True
128
+ example["constraint_mask"] = constraint_mask
129
+ return example
130
+
131
+ def collater(self, samples, pad_to_length=None):
132
+ """Merge a list of samples to form a mini-batch.
133
+ Args:
134
+ samples (List[dict]): samples to collate
135
+ Returns:
136
+ dict: a mini-batch containing the data of the task
137
+ """
138
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlu_data/mnli_dataset.py ADDED
@@ -0,0 +1,143 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ ref_dict = None
33
+ if samples[0].get("ref_dict", None) is not None:
34
+ ref_dict = np.array([s['ref_dict'] for s in samples])
35
+
36
+ constraint_masks = None
37
+ if samples[0].get("constraint_mask", None) is not None:
38
+ constraint_masks = merge("constraint_mask")
39
+
40
+ prev_output_tokens = None
41
+ target = None
42
+ if samples[0].get("target", None) is not None:
43
+ target = merge("target")
44
+ tgt_lengths = torch.LongTensor(
45
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
46
+ )
47
+ ntokens = tgt_lengths.sum().item()
48
+
49
+ if samples[0].get("prev_output_tokens", None) is not None:
50
+ prev_output_tokens = merge("prev_output_tokens")
51
+ else:
52
+ ntokens = src_lengths.sum().item()
53
+
54
+ batch = {
55
+ "nsentences": len(samples),
56
+ "ntokens": ntokens,
57
+ "net_input": {
58
+ "src_tokens": src_tokens,
59
+ "src_lengths": src_lengths,
60
+ "prev_output_tokens": prev_output_tokens
61
+ },
62
+ "ref_dict": ref_dict,
63
+ "constraint_masks": constraint_masks,
64
+ "target": target,
65
+ }
66
+
67
+ return batch
68
+
69
+
70
+ class MNLIDataset(OFADataset):
71
+ def __init__(
72
+ self,
73
+ split,
74
+ dataset,
75
+ bpe,
76
+ src_dict,
77
+ tgt_dict=None,
78
+ max_src_length=512,
79
+ max_tgt_length=30,
80
+ constraint_trie=None,
81
+ prompt_type="none"
82
+ ):
83
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
84
+ self.max_src_length = max_src_length
85
+ self.max_tgt_length = max_tgt_length
86
+ self.constraint_trie = constraint_trie
87
+ self.prompt_type = prompt_type
88
+
89
+ def __getitem__(self, index):
90
+ sentence1, sentence2, label = self.dataset[index]
91
+ if label == '0':
92
+ label = 'maybe'
93
+ elif label == '1':
94
+ label = 'yes'
95
+ elif label == '2':
96
+ label = 'no'
97
+ else:
98
+ raise NotImplementedError
99
+
100
+ sentence1 = ' '.join(sentence1.lower().strip().split()[:self.max_src_length])
101
+ sentence2 = ' '.join(sentence2.lower().strip().split()[:self.max_src_length])
102
+ src_item = self.encode_text(
103
+ ' can text1 " {} " imply text2 " {} "?'.format(sentence1, sentence2)
104
+ )
105
+ tgt_item = self.encode_text(" {}".format(label))
106
+ assert tgt_item.size(0) == 1
107
+ ref_dict = {label: 1.0}
108
+
109
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
110
+ if self.prompt_type == 'none':
111
+ prev_output_item = self.bos_item
112
+ target_item = tgt_item
113
+ elif self.prompt_type == 'src':
114
+ prev_output_item = src_item.clone()
115
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
116
+ elif self.prompt_type == 'prev_output':
117
+ prev_output_item = src_item[:-1].clone()
118
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
119
+ else:
120
+ raise NotImplementedError
121
+ target_item[:-1] = self.tgt_dict.pad()
122
+
123
+ example = {
124
+ "source": src_item,
125
+ "target": target_item,
126
+ "prev_output_tokens": prev_output_item,
127
+ "ref_dict": ref_dict,
128
+ }
129
+ if self.constraint_trie is not None:
130
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
131
+ constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
132
+ constraint_mask[-1][constraint_nodes] = True
133
+ example["constraint_mask"] = constraint_mask
134
+ return example
135
+
136
+ def collater(self, samples, pad_to_length=None):
137
+ """Merge a list of samples to form a mini-batch.
138
+ Args:
139
+ samples (List[dict]): samples to collate
140
+ Returns:
141
+ dict: a mini-batch containing the data of the task
142
+ """
143
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlu_data/mrpc_dataset.py ADDED
@@ -0,0 +1,141 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ ref_dict = None
33
+ if samples[0].get("ref_dict", None) is not None:
34
+ ref_dict = np.array([s['ref_dict'] for s in samples])
35
+
36
+ constraint_masks = None
37
+ if samples[0].get("constraint_mask", None) is not None:
38
+ constraint_masks = merge("constraint_mask")
39
+
40
+ prev_output_tokens = None
41
+ target = None
42
+ if samples[0].get("target", None) is not None:
43
+ target = merge("target")
44
+ tgt_lengths = torch.LongTensor(
45
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
46
+ )
47
+ ntokens = tgt_lengths.sum().item()
48
+
49
+ if samples[0].get("prev_output_tokens", None) is not None:
50
+ prev_output_tokens = merge("prev_output_tokens")
51
+ else:
52
+ ntokens = src_lengths.sum().item()
53
+
54
+ batch = {
55
+ "nsentences": len(samples),
56
+ "ntokens": ntokens,
57
+ "net_input": {
58
+ "src_tokens": src_tokens,
59
+ "src_lengths": src_lengths,
60
+ "prev_output_tokens": prev_output_tokens
61
+ },
62
+ "ref_dict": ref_dict,
63
+ "constraint_masks": constraint_masks,
64
+ "target": target,
65
+ }
66
+
67
+ return batch
68
+
69
+
70
+ class MRPCDataset(OFADataset):
71
+ def __init__(
72
+ self,
73
+ split,
74
+ dataset,
75
+ bpe,
76
+ src_dict,
77
+ tgt_dict=None,
78
+ max_src_length=512,
79
+ max_tgt_length=30,
80
+ constraint_trie=None,
81
+ prompt_type="none"
82
+ ):
83
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
84
+ self.max_src_length = max_src_length
85
+ self.max_tgt_length = max_tgt_length
86
+ self.constraint_trie = constraint_trie
87
+ self.prompt_type = prompt_type
88
+
89
+ def __getitem__(self, index):
90
+ sentence1, sentence2, label = self.dataset[index]
91
+ if label == '0':
92
+ label = 'no'
93
+ elif label == '1':
94
+ label = 'yes'
95
+ else:
96
+ raise NotImplementedError
97
+
98
+ sentence1 = ' '.join(sentence1.lower().strip().split()[:self.max_src_length])
99
+ sentence2 = ' '.join(sentence2.lower().strip().split()[:self.max_src_length])
100
+ src_item = self.encode_text(
101
+ ' does text1 " {} " and text2 " {} " have the same semantics?'.format(sentence1, sentence2),
102
+ )
103
+ tgt_item = self.encode_text(" {}".format(label))
104
+ assert tgt_item.size(0) == 1
105
+ ref_dict = {label: 1.0}
106
+
107
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
108
+ if self.prompt_type == 'none':
109
+ prev_output_item = self.bos_item
110
+ target_item = tgt_item
111
+ elif self.prompt_type == 'src':
112
+ prev_output_item = src_item.clone()
113
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
114
+ elif self.prompt_type == 'prev_output':
115
+ prev_output_item = src_item[:-1].clone()
116
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
117
+ else:
118
+ raise NotImplementedError
119
+ target_item[:-1] = self.tgt_dict.pad()
120
+
121
+ example = {
122
+ "source": src_item,
123
+ "target": target_item,
124
+ "prev_output_tokens": prev_output_item,
125
+ "ref_dict": ref_dict,
126
+ }
127
+ if self.constraint_trie is not None:
128
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
129
+ constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
130
+ constraint_mask[-1][constraint_nodes] = True
131
+ example["constraint_mask"] = constraint_mask
132
+ return example
133
+
134
+ def collater(self, samples, pad_to_length=None):
135
+ """Merge a list of samples to form a mini-batch.
136
+ Args:
137
+ samples (List[dict]): samples to collate
138
+ Returns:
139
+ dict: a mini-batch containing the data of the task
140
+ """
141
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlu_data/qnli_dataset.py ADDED
@@ -0,0 +1,141 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ ref_dict = None
33
+ if samples[0].get("ref_dict", None) is not None:
34
+ ref_dict = np.array([s['ref_dict'] for s in samples])
35
+
36
+ constraint_masks = None
37
+ if samples[0].get("constraint_mask", None) is not None:
38
+ constraint_masks = merge("constraint_mask")
39
+
40
+ prev_output_tokens = None
41
+ target = None
42
+ if samples[0].get("target", None) is not None:
43
+ target = merge("target")
44
+ tgt_lengths = torch.LongTensor(
45
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
46
+ )
47
+ ntokens = tgt_lengths.sum().item()
48
+
49
+ if samples[0].get("prev_output_tokens", None) is not None:
50
+ prev_output_tokens = merge("prev_output_tokens")
51
+ else:
52
+ ntokens = src_lengths.sum().item()
53
+
54
+ batch = {
55
+ "nsentences": len(samples),
56
+ "ntokens": ntokens,
57
+ "net_input": {
58
+ "src_tokens": src_tokens,
59
+ "src_lengths": src_lengths,
60
+ "prev_output_tokens": prev_output_tokens
61
+ },
62
+ "ref_dict": ref_dict,
63
+ "constraint_masks": constraint_masks,
64
+ "target": target,
65
+ }
66
+
67
+ return batch
68
+
69
+
70
+ class QNLIDataset(OFADataset):
71
+ def __init__(
72
+ self,
73
+ split,
74
+ dataset,
75
+ bpe,
76
+ src_dict,
77
+ tgt_dict=None,
78
+ max_src_length=512,
79
+ max_tgt_length=30,
80
+ constraint_trie=None,
81
+ prompt_type="none"
82
+ ):
83
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
84
+ self.max_src_length = max_src_length
85
+ self.max_tgt_length = max_tgt_length
86
+ self.constraint_trie = constraint_trie
87
+ self.prompt_type = prompt_type
88
+
89
+ def __getitem__(self, index):
90
+ question, sentence, label = self.dataset[index]
91
+ if label == '0' or label == 'not_entailment':
92
+ label = 'no'
93
+ elif label == '1' or label == 'entailment':
94
+ label = 'yes'
95
+ else:
96
+ raise NotImplementedError
97
+
98
+ question = ' '.join(question.lower().strip().split()[:self.max_src_length])
99
+ sentence = ' '.join(sentence.lower().strip().split()[:self.max_src_length])
100
+ src_item = self.encode_text(
101
+ ' does " {} " contain the answer to question " {} "?'.format(sentence, question)
102
+ )
103
+ tgt_item = self.encode_text(" {}".format(label))
104
+ assert tgt_item.size(0) == 1
105
+ ref_dict = {label: 1.0}
106
+
107
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
108
+ if self.prompt_type == 'none':
109
+ prev_output_item = self.bos_item
110
+ target_item = tgt_item
111
+ elif self.prompt_type == 'src':
112
+ prev_output_item = src_item.clone()
113
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
114
+ elif self.prompt_type == 'prev_output':
115
+ prev_output_item = src_item[:-1].clone()
116
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
117
+ else:
118
+ raise NotImplementedError
119
+ target_item[:-1] = self.tgt_dict.pad()
120
+
121
+ example = {
122
+ "source": src_item,
123
+ "target": target_item,
124
+ "prev_output_tokens": prev_output_item,
125
+ "ref_dict": ref_dict,
126
+ }
127
+ if self.constraint_trie is not None:
128
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
129
+ constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
130
+ constraint_mask[-1][constraint_nodes] = True
131
+ example["constraint_mask"] = constraint_mask
132
+ return example
133
+
134
+ def collater(self, samples, pad_to_length=None):
135
+ """Merge a list of samples to form a mini-batch.
136
+ Args:
137
+ samples (List[dict]): samples to collate
138
+ Returns:
139
+ dict: a mini-batch containing the data of the task
140
+ """
141
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlu_data/qqp_dataset.py ADDED
@@ -0,0 +1,141 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ ref_dict = None
33
+ if samples[0].get("ref_dict", None) is not None:
34
+ ref_dict = np.array([s['ref_dict'] for s in samples])
35
+
36
+ constraint_masks = None
37
+ if samples[0].get("constraint_mask", None) is not None:
38
+ constraint_masks = merge("constraint_mask")
39
+
40
+ prev_output_tokens = None
41
+ target = None
42
+ if samples[0].get("target", None) is not None:
43
+ target = merge("target")
44
+ tgt_lengths = torch.LongTensor(
45
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
46
+ )
47
+ ntokens = tgt_lengths.sum().item()
48
+
49
+ if samples[0].get("prev_output_tokens", None) is not None:
50
+ prev_output_tokens = merge("prev_output_tokens")
51
+ else:
52
+ ntokens = src_lengths.sum().item()
53
+
54
+ batch = {
55
+ "nsentences": len(samples),
56
+ "ntokens": ntokens,
57
+ "net_input": {
58
+ "src_tokens": src_tokens,
59
+ "src_lengths": src_lengths,
60
+ "prev_output_tokens": prev_output_tokens
61
+ },
62
+ "ref_dict": ref_dict,
63
+ "constraint_masks": constraint_masks,
64
+ "target": target,
65
+ }
66
+
67
+ return batch
68
+
69
+
70
+ class QQPDataset(OFADataset):
71
+ def __init__(
72
+ self,
73
+ split,
74
+ dataset,
75
+ bpe,
76
+ src_dict,
77
+ tgt_dict=None,
78
+ max_src_length=512,
79
+ max_tgt_length=30,
80
+ constraint_trie=None,
81
+ prompt_type="none"
82
+ ):
83
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
84
+ self.max_src_length = max_src_length
85
+ self.max_tgt_length = max_tgt_length
86
+ self.constraint_trie = constraint_trie
87
+ self.prompt_type = prompt_type
88
+
89
+ def __getitem__(self, index):
90
+ question1, question2, label = self.dataset[index]
91
+ if label == '0':
92
+ label = 'no'
93
+ elif label == '1':
94
+ label = 'yes'
95
+ else:
96
+ raise NotImplementedError
97
+
98
+ question1 = ' '.join(question1.lower().strip().split()[:self.max_src_length])
99
+ question2 = ' '.join(question2.lower().strip().split()[:self.max_src_length])
100
+ src_item = self.encode_text(
101
+ ' is question " {} " and question " {} " equivalent?'.format(question1, question2)
102
+ )
103
+ tgt_item = self.encode_text(" {}".format(label))
104
+ assert tgt_item.size(0) == 1
105
+ ref_dict = {label: 1.0}
106
+
107
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
108
+ if self.prompt_type == 'none':
109
+ prev_output_item = self.bos_item
110
+ target_item = tgt_item
111
+ elif self.prompt_type == 'src':
112
+ prev_output_item = src_item.clone()
113
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
114
+ elif self.prompt_type == 'prev_output':
115
+ prev_output_item = src_item[:-1].clone()
116
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
117
+ else:
118
+ raise NotImplementedError
119
+ target_item[:-1] = self.tgt_dict.pad()
120
+
121
+ example = {
122
+ "source": src_item,
123
+ "target": target_item,
124
+ "prev_output_tokens": prev_output_item,
125
+ "ref_dict": ref_dict,
126
+ }
127
+ if self.constraint_trie is not None:
128
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
129
+ constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
130
+ constraint_mask[-1][constraint_nodes] = True
131
+ example["constraint_mask"] = constraint_mask
132
+ return example
133
+
134
+ def collater(self, samples, pad_to_length=None):
135
+ """Merge a list of samples to form a mini-batch.
136
+ Args:
137
+ samples (List[dict]): samples to collate
138
+ Returns:
139
+ dict: a mini-batch containing the data of the task
140
+ """
141
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlu_data/rte_dataset.py ADDED
@@ -0,0 +1,141 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ ref_dict = None
33
+ if samples[0].get("ref_dict", None) is not None:
34
+ ref_dict = np.array([s['ref_dict'] for s in samples])
35
+
36
+ constraint_masks = None
37
+ if samples[0].get("constraint_mask", None) is not None:
38
+ constraint_masks = merge("constraint_mask")
39
+
40
+ prev_output_tokens = None
41
+ target = None
42
+ if samples[0].get("target", None) is not None:
43
+ target = merge("target")
44
+ tgt_lengths = torch.LongTensor(
45
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
46
+ )
47
+ ntokens = tgt_lengths.sum().item()
48
+
49
+ if samples[0].get("prev_output_tokens", None) is not None:
50
+ prev_output_tokens = merge("prev_output_tokens")
51
+ else:
52
+ ntokens = src_lengths.sum().item()
53
+
54
+ batch = {
55
+ "nsentences": len(samples),
56
+ "ntokens": ntokens,
57
+ "net_input": {
58
+ "src_tokens": src_tokens,
59
+ "src_lengths": src_lengths,
60
+ "prev_output_tokens": prev_output_tokens
61
+ },
62
+ "ref_dict": ref_dict,
63
+ "constraint_masks": constraint_masks,
64
+ "target": target,
65
+ }
66
+
67
+ return batch
68
+
69
+
70
+ class RTEDataset(OFADataset):
71
+ def __init__(
72
+ self,
73
+ split,
74
+ dataset,
75
+ bpe,
76
+ src_dict,
77
+ tgt_dict=None,
78
+ max_src_length=512,
79
+ max_tgt_length=30,
80
+ constraint_trie=None,
81
+ prompt_type="none"
82
+ ):
83
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
84
+ self.max_src_length = max_src_length
85
+ self.max_tgt_length = max_tgt_length
86
+ self.constraint_trie = constraint_trie
87
+ self.prompt_type = prompt_type
88
+
89
+ def __getitem__(self, index):
90
+ sentence1, sentence2, label = self.dataset[index]
91
+ if label == 'not_entailment':
92
+ label = 'no'
93
+ elif label == 'entailment':
94
+ label = 'yes'
95
+ else:
96
+ raise NotImplementedError
97
+
98
+ sentence1 = ' '.join(sentence1.lower().strip().split()[:self.max_src_length])
99
+ sentence2 = ' '.join(sentence2.lower().strip().split()[:self.max_src_length])
100
+ src_item = self.encode_text(
101
+ ' can text1 " {} " imply text2 " {} "?'.format(sentence1, sentence2),
102
+ )
103
+ tgt_item = self.encode_text(" {}".format(label))
104
+ assert tgt_item.size(0) == 1
105
+ ref_dict = {label: 1.0}
106
+
107
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
108
+ if self.prompt_type == 'none':
109
+ prev_output_item = self.bos_item
110
+ target_item = tgt_item
111
+ elif self.prompt_type == 'src':
112
+ prev_output_item = src_item.clone()
113
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
114
+ elif self.prompt_type == 'prev_output':
115
+ prev_output_item = src_item[:-1].clone()
116
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
117
+ else:
118
+ raise NotImplementedError
119
+ target_item[:-1] = self.tgt_dict.pad()
120
+
121
+ example = {
122
+ "source": src_item,
123
+ "target": target_item,
124
+ "prev_output_tokens": prev_output_item,
125
+ "ref_dict": ref_dict,
126
+ }
127
+ if self.constraint_trie is not None:
128
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
129
+ constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
130
+ constraint_mask[-1][constraint_nodes] = True
131
+ example["constraint_mask"] = constraint_mask
132
+ return example
133
+
134
+ def collater(self, samples, pad_to_length=None):
135
+ """Merge a list of samples to form a mini-batch.
136
+ Args:
137
+ samples (List[dict]): samples to collate
138
+ Returns:
139
+ dict: a mini-batch containing the data of the task
140
+ """
141
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/nlu_data/sst2_dataset.py ADDED
@@ -0,0 +1,138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import warnings
8
+ import torch
9
+ import numpy as np
10
+
11
+ from data import data_utils
12
+ from data.ofa_dataset import OFADataset
13
+
14
+ logger = logging.getLogger(__name__)
15
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
16
+
17
+
18
+ def collate(samples, pad_idx, eos_idx):
19
+ if len(samples) == 0:
20
+ return {}
21
+
22
+ def merge(key):
23
+ return data_utils.collate_tokens(
24
+ [s[key] for s in samples],
25
+ pad_idx,
26
+ eos_idx=eos_idx,
27
+ )
28
+
29
+ src_tokens = merge("source")
30
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
31
+
32
+ ref_dict = None
33
+ if samples[0].get("ref_dict", None) is not None:
34
+ ref_dict = np.array([s['ref_dict'] for s in samples])
35
+
36
+ constraint_masks = None
37
+ if samples[0].get("constraint_mask", None) is not None:
38
+ constraint_masks = merge("constraint_mask")
39
+
40
+ prev_output_tokens = None
41
+ target = None
42
+ if samples[0].get("target", None) is not None:
43
+ target = merge("target")
44
+ tgt_lengths = torch.LongTensor(
45
+ [s["target"].ne(pad_idx).long().sum() for s in samples]
46
+ )
47
+ ntokens = tgt_lengths.sum().item()
48
+
49
+ if samples[0].get("prev_output_tokens", None) is not None:
50
+ prev_output_tokens = merge("prev_output_tokens")
51
+ else:
52
+ ntokens = src_lengths.sum().item()
53
+
54
+ batch = {
55
+ "nsentences": len(samples),
56
+ "ntokens": ntokens,
57
+ "net_input": {
58
+ "src_tokens": src_tokens,
59
+ "src_lengths": src_lengths,
60
+ "prev_output_tokens": prev_output_tokens
61
+ },
62
+ "ref_dict": ref_dict,
63
+ "constraint_masks": constraint_masks,
64
+ "target": target,
65
+ }
66
+
67
+ return batch
68
+
69
+
70
+ class SST2Dataset(OFADataset):
71
+ def __init__(
72
+ self,
73
+ split,
74
+ dataset,
75
+ bpe,
76
+ src_dict,
77
+ tgt_dict=None,
78
+ max_src_length=512,
79
+ max_tgt_length=30,
80
+ constraint_trie=None,
81
+ prompt_type="none"
82
+ ):
83
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
84
+ self.max_src_length = max_src_length
85
+ self.max_tgt_length = max_tgt_length
86
+ self.constraint_trie = constraint_trie
87
+ self.prompt_type = prompt_type
88
+
89
+ def __getitem__(self, index):
90
+ sentence, label = self.dataset[index]
91
+ if label == '0':
92
+ label = 'negative'
93
+ elif label == '1':
94
+ label = 'positive'
95
+ else:
96
+ raise NotImplementedError
97
+
98
+ sentence = ' '.join(sentence.lower().strip().split()[:self.max_src_length])
99
+ src_item = self.encode_text(' is the sentiment of text " {} " positive or negative?'.format(sentence))
100
+ tgt_item = self.encode_text(" {}".format(label))
101
+ assert tgt_item.size(0) == 1
102
+ ref_dict = {label: 1.0}
103
+
104
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
105
+ if self.prompt_type == 'none':
106
+ prev_output_item = self.bos_item
107
+ target_item = tgt_item
108
+ elif self.prompt_type == 'src':
109
+ prev_output_item = src_item.clone()
110
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
111
+ elif self.prompt_type == 'prev_output':
112
+ prev_output_item = src_item[:-1].clone()
113
+ target_item = torch.cat([prev_output_item[1:], tgt_item])
114
+ else:
115
+ raise NotImplementedError
116
+ target_item[:-1] = self.tgt_dict.pad()
117
+
118
+ example = {
119
+ "source": src_item,
120
+ "target": target_item,
121
+ "prev_output_tokens": prev_output_item,
122
+ "ref_dict": ref_dict,
123
+ }
124
+ if self.constraint_trie is not None:
125
+ constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
126
+ constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
127
+ constraint_mask[-1][constraint_nodes] = True
128
+ example["constraint_mask"] = constraint_mask
129
+ return example
130
+
131
+ def collater(self, samples, pad_to_length=None):
132
+ """Merge a list of samples to form a mini-batch.
133
+ Args:
134
+ samples (List[dict]): samples to collate
135
+ Returns:
136
+ dict: a mini-batch containing the data of the task
137
+ """
138
+ return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
data/ofa_dataset.py ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ import logging
7
+ import re
8
+ import torch.utils.data
9
+ from fairseq.data import FairseqDataset
10
+
11
+ logger = logging.getLogger(__name__)
12
+
13
+
14
+ class OFADataset(FairseqDataset):
15
+ def __init__(self, split, dataset, bpe, src_dict, tgt_dict):
16
+ self.split = split
17
+ self.dataset = dataset
18
+ self.bpe = bpe
19
+ self.src_dict = src_dict
20
+ self.tgt_dict = tgt_dict
21
+
22
+ self.bos = src_dict.bos()
23
+ self.eos = src_dict.eos()
24
+ self.pad = src_dict.pad()
25
+ self.bos_item = torch.LongTensor([self.bos])
26
+ self.eos_item = torch.LongTensor([self.eos])
27
+
28
+ def __len__(self):
29
+ return len(self.dataset)
30
+
31
+ def encode_text(self, text, length=None, append_bos=False, append_eos=False, use_bpe=True):
32
+ s = self.tgt_dict.encode_line(
33
+ line=self.bpe.encode(text) if use_bpe else text,
34
+ add_if_not_exist=False,
35
+ append_eos=False
36
+ ).long()
37
+ if length is not None:
38
+ s = s[:length]
39
+ if append_bos:
40
+ s = torch.cat([self.bos_item, s])
41
+ if append_eos:
42
+ s = torch.cat([s, self.eos_item])
43
+ return s
44
+
45
+ def pre_question(self, question, max_ques_words=None):
46
+ question = question.lower().lstrip(",.!?*#:;~").replace('-', ' ').replace('/', ' ')
47
+
48
+ question = re.sub(
49
+ r"\s{2,}",
50
+ ' ',
51
+ question,
52
+ )
53
+ question = question.rstrip('\n')
54
+ question = question.strip(' ')
55
+
56
+ # truncate question
57
+ question_words = question.split(' ')
58
+ if max_ques_words is not None and len(question_words) > max_ques_words:
59
+ question = ' '.join(question_words[:max_ques_words])
60
+
61
+ return question
62
+
63
+ def pre_caption(self, caption, max_words=None):
64
+ caption = caption.lower().lstrip(",.!?*#:;~").replace('-', ' ').replace('/', ' ').replace('<person>', 'person')
65
+
66
+ caption = re.sub(
67
+ r"\s{2,}",
68
+ ' ',
69
+ caption,
70
+ )
71
+ caption = caption.rstrip('\n')
72
+ caption = caption.strip(' ')
73
+
74
+ # truncate caption
75
+ caption_words = caption.split(' ')
76
+ if max_words is not None and len(caption_words) > max_words:
77
+ caption = ' '.join(caption_words[:max_words])
78
+
79
+ return caption
data/pretrain_data/unify_dataset.py ADDED
@@ -0,0 +1,636 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 The OFA-Sys Team.
2
+ # All rights reserved.
3
+ # This source code is licensed under the Apache 2.0 license
4
+ # found in the LICENSE file in the root directory.
5
+
6
+ from io import BytesIO
7
+
8
+ import math
9
+ import logging
10
+ import random
11
+ import warnings
12
+
13
+ import numpy as np
14
+ import torch
15
+ import base64
16
+ from torchvision import transforms
17
+
18
+ from PIL import Image, ImageFile
19
+
20
+ from data import data_utils
21
+ from data.ofa_dataset import OFADataset
22
+ from utils.vision_helper import RandomAugment
23
+ import utils.transforms as T
24
+
25
+ ImageFile.LOAD_TRUNCATED_IMAGES = True
26
+ ImageFile.MAX_IMAGE_PIXELS = None
27
+ Image.MAX_IMAGE_PIXELS = None
28
+
29
+ logger = logging.getLogger(__name__)
30
+ warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
31
+
32
+
33
+ def get_whole_word_mask(bpe, dictionary):
34
+ if bpe is not None:
35
+
36
+ def is_beginning_of_word(i):
37
+ if i < dictionary.nspecial:
38
+ # special elements are always considered beginnings
39
+ return True
40
+ tok = dictionary[i]
41
+ if tok.startswith("madeupword"):
42
+ return True
43
+ try:
44
+ return bpe.is_beginning_of_word(tok)
45
+ except ValueError:
46
+ return True
47
+
48
+ mask_whole_words = torch.ByteTensor(
49
+ list(map(is_beginning_of_word, range(len(dictionary))))
50
+ )
51
+ return mask_whole_words
52
+ return None
53
+
54
+
55
+ def collate(samples, pad_idx, eos_idx):
56
+ if len(samples) == 0:
57
+ return {}
58
+
59
+ def merge(key):
60
+ return data_utils.collate_tokens(
61
+ [s[key] for s in samples],
62
+ pad_idx,
63
+ eos_idx=eos_idx,
64
+ )
65
+
66
+ id = np.array([s["id"] for s in samples])
67
+ src_tokens = merge("source")
68
+ src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
69
+
70
+ patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
71
+ patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
72
+
73
+ code_masks = None
74
+ if samples[0].get("code_mask", None) is not None:
75
+ code_masks = torch.cat([sample['code_mask'] for sample in samples])
76
+
77
+ conf = torch.cat([s['conf'] for s in samples], dim=0)
78
+
79
+ prev_output_tokens = None
80
+ target = None
81
+ if samples[0].get("target", None) is not None:
82
+ target = merge("target")
83
+ tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
84
+ ntokens = tgt_lengths.sum().item()
85
+
86
+ if samples[0].get("prev_output_tokens", None) is not None:
87
+ prev_output_tokens = merge("prev_output_tokens")
88
+ else:
89
+ ntokens = src_lengths.sum().item()
90
+
91
+ batch = {
92
+ "id": id,
93
+ "nsentences": len(samples),
94
+ "ntokens": ntokens,
95
+ "net_input": {
96
+ "src_tokens": src_tokens,
97
+ "src_lengths": src_lengths,
98
+ "patch_images": patch_images,
99
+ "patch_masks": patch_masks,
100
+ "code_masks": code_masks,
101
+ "prev_output_tokens": prev_output_tokens
102
+ },
103
+ "target": target,
104
+ "conf": conf
105
+ }
106
+
107
+ return batch
108
+
109
+
110
+ class UnifyDataset(OFADataset):
111
+ def __init__(
112
+ self,
113
+ split,
114
+ dataset,
115
+ bpe,
116
+ src_dict,
117
+ tgt_dict=None,
118
+ max_src_length=128,
119
+ max_tgt_length=30,
120
+ seed=7,
121
+ code_dict_size=8192,
122
+ num_bins=1000,
123
+ patch_image_size=384,
124
+ code_image_size=128,
125
+ pure_text_dataset=None,
126
+ pure_image_dataset=None,
127
+ detection_dataset=None,
128
+ all_object_list=None,
129
+ all_caption_list=None,
130
+ type2ans_dict=None,
131
+ ans2type_dict=None,
132
+ max_image_size=512,
133
+ mask_ratio=0.3,
134
+ random_ratio=0.0,
135
+ keep_ratio=0.0,
136
+ mask_length="span-poisson",
137
+ poisson_lambda=3.0,
138
+ replace_length=1
139
+ ):
140
+ super().__init__(split, dataset, bpe, src_dict, tgt_dict)
141
+ self.max_src_length = max_src_length
142
+ self.max_tgt_length = max_tgt_length
143
+ self.seed = seed
144
+ self.code_dict_size = code_dict_size
145
+ self.num_bins = num_bins
146
+ self.patch_image_size = patch_image_size
147
+ self.code_image_size = code_image_size
148
+
149
+ self.pure_text_dataset = pure_text_dataset
150
+ self.pure_image_dataset = pure_image_dataset
151
+ self.detection_dataset = detection_dataset
152
+ self.epoch = 0
153
+
154
+ self.all_object_list = all_object_list
155
+ self.all_caption_list = all_caption_list
156
+ self.type2ans_dict = type2ans_dict
157
+ self.ans2type_dict = ans2type_dict
158
+
159
+ self.mask_ratio = mask_ratio
160
+ self.random_ratio = random_ratio
161
+ self.keep_ratio = keep_ratio
162
+ self.mask_length = mask_length
163
+ self.poisson_lambda = poisson_lambda
164
+ self.replace_length = replace_length
165
+ if self.replace_length not in [-1, 0, 1]:
166
+ raise ValueError(f"invalid arg: replace_length={self.replace_length}")
167
+ if self.mask_length not in ["subword", "word", "span-poisson"]:
168
+ raise ValueError(f"invalid arg: mask-length={self.mask_length}")
169
+ if self.mask_length == "subword" and self.replace_length not in [0, 1]:
170
+ raise ValueError(f"if using subwords, use replace-length=1 or 0")
171
+
172
+ self.mask_idx = src_dict.index("<mask>")
173
+ self.mask_whole_word = (
174
+ get_whole_word_mask(self.bpe, self.src_dict)
175
+ if self.mask_length != "subword"
176
+ else None
177
+ )
178
+ self.mask_span_distribution = None
179
+ if self.mask_length == "span-poisson":
180
+ _lambda = self.poisson_lambda
181
+ lambda_to_the_k = 1
182
+ e_to_the_minus_lambda = math.exp(-_lambda)
183
+ k_factorial = 1
184
+ ps = []
185
+ for k in range(0, 128):
186
+ ps.append(e_to_the_minus_lambda * lambda_to_the_k / k_factorial)
187
+ lambda_to_the_k *= _lambda
188
+ k_factorial *= k + 1
189
+ if ps[-1] < 0.0000001:
190
+ break
191
+ ps = torch.FloatTensor(ps)
192
+ self.mask_span_distribution = torch.distributions.Categorical(ps)
193
+
194
+ self.pos_tgt_item = self.encode_text(" yes")
195
+ self.neg_tgt_item = self.encode_text(" no")
196
+
197
+ self.mask_left = self.mask_top = int(0.5 * self.code_image_size)
198
+ self.mask_right = self.mask_bottom = int(1.5 * self.code_image_size)
199
+ self.mask_ids = [
200
+ i*self.code_image_size*2+j
201
+ for i in range(self.code_image_size*2) for j in range(self.code_image_size*2)
202
+ if not (self.mask_left <= i < self.mask_right and self.mask_top <= j < self.mask_bottom)
203
+ ]
204
+
205
+ scales = np.arange(patch_image_size, 481).tolist()
206
+
207
+ # for image-text pair
208
+ self.patch_resize_transform = transforms.Compose([
209
+ T.RandomResize(scales, max_size=672),
210
+ transforms.CenterCrop(patch_image_size),
211
+ RandomAugment(2, 7, isPIL=True, augs=['Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness',
212
+ 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']),
213
+ transforms.ToTensor(),
214
+ transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
215
+ ])
216
+ # for pure image
217
+ self.patch_crop_transform = transforms.Compose([
218
+ transforms.ToTensor(),
219
+ transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
220
+ ])
221
+ # for detection
222
+ self.detection_transform = T.Compose([
223
+ T.RandomHorizontalFlip(),
224
+ T.LargeScaleJitter(output_size=self.code_image_size*2, aug_scale_min=1.0, aug_scale_max=1.5),
225
+ T.ToTensor(),
226
+ T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_image_size=max_image_size)
227
+ ])
228
+ # for visual grounding
229
+ self.visual_grounding_transform = T.Compose([
230
+ T.RandomResize(scales, max_size=672),
231
+ T.ObjectCenterCrop((patch_image_size, patch_image_size)),
232
+ T.ToTensor(),
233
+ T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_image_size=max_image_size)
234
+ ])
235
+
236
+ def set_epoch(self, epoch, **unused):
237
+ self.epoch = epoch
238
+
239
+ def get_negative_caption(self, caption, gt_objects):
240
+ prob = random.random()
241
+ if gt_objects is not None and gt_objects != '' and prob > 0.6:
242
+ gt_object = random.choice(gt_objects.strip().split('&&'))
243
+ negative_object = random.choice(self.all_object_list[:-1])
244
+ negative_object = self.all_object_list[-1] if negative_object == gt_object else negative_object
245
+ negative_caption = caption.replace(gt_object, negative_object)
246
+ else:
247
+ negative_caption = random.choice(self.all_caption_list)
248
+ return negative_caption
249
+
250
+ def get_negative_answer(self, answer, conf):
251
+ prob = random.random()
252
+ if conf > (prob + 0.1) and answer in self.ans2type_dict:
253
+ negative_answer_type = self.ans2type_dict[answer]
254
+ if negative_answer_type == 'how many' and answer.isdigit() and prob > 0.5:
255
+ negative_answer = int(answer) + random.choice([-1, 1]) if answer != 0 else 1
256
+ else:
257
+ negative_answer_list = self.type2ans_dict[negative_answer_type]
258
+ negative_answer = random.choice(negative_answer_list[:-1])
259
+ negative_answer = negative_answer_list[-1] if negative_answer == answer else negative_answer
260
+ return negative_answer
261
+
262
+ negative_answer_list = self.type2ans_dict['other']
263
+ negative_answer = random.choice(negative_answer_list[:-1])
264
+ negative_answer = negative_answer_list[-1] if negative_answer == answer else negative_answer
265
+ return negative_answer
266
+
267
+ def process_image_text_pair(self, index):
268
+ uniq_id, image, caption, question, refs, gt_objects, dataset_name, type = self.dataset[index]
269
+
270
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image))).convert("RGB")
271
+ patch_image = self.patch_resize_transform(image) if type != 'visual_grounding' else None
272
+ patch_mask = torch.tensor([True])
273
+ conf = torch.tensor([1.0])
274
+ if type == 'caption':
275
+ tgt_caption = self.pre_caption(caption, self.max_tgt_length)
276
+ pos_src_caption = self.pre_caption(caption, self.max_src_length)
277
+ neg_src_caption = self.pre_caption(self.get_negative_caption(caption, gt_objects), self.max_src_length)
278
+ src_item = self.encode_text(" what does the image describe?")
279
+ tgt_item = self.encode_text(" {}".format(tgt_caption))
280
+ pos_src_item = self.encode_text(' does the image describe " {} "?'.format(pos_src_caption))
281
+ neg_src_item = self.encode_text(' does the image describe " {} "?'.format(neg_src_caption))
282
+ elif type == 'qa':
283
+ question = self.pre_question(question, self.max_src_length)
284
+ ref_dict = {item.split('|!+')[1]: float(item.split('|!+')[0]) for item in refs.split('&&')}
285
+ answer = max(ref_dict, key=ref_dict.get)
286
+ conf = ref_dict[answer]
287
+ src_item = self.encode_text(" {}".format(question))
288
+ tgt_item = self.encode_text(" {}".format(answer))
289
+ conf = torch.tensor([conf])
290
+ pos_src_item = self.encode_text(' what is the answer to question " {} ". is " {} "?'.format(question, answer))
291
+ neg_src_item = self.encode_text(
292
+ ' what is the answer to question " {} ". is " {} "?'.format(question, self.get_negative_answer(answer, conf))
293
+ )
294
+ elif type == 'visual_grounding':
295
+ conf = torch.tensor([1.0])
296
+ w, h = image.size
297
+ boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
298
+ x0, y0, x1, y1 = refs.strip().split(',')
299
+ boxes_target["boxes"] = torch.tensor([[float(x0), float(y0), float(x1), float(y1)]])
300
+ boxes_target["labels"] = np.array([0])
301
+ boxes_target["area"] = torch.tensor([(float(x1) - float(x0)) * (float(y1) - float(y0))])
302
+ patch_image, boxes_target = self.visual_grounding_transform(image, boxes_target)
303
+ quant_x0 = "<bin_{}>".format(int((boxes_target["boxes"][0][0] * (self.num_bins - 1)).round()))
304
+ quant_y0 = "<bin_{}>".format(int((boxes_target["boxes"][0][1] * (self.num_bins - 1)).round()))
305
+ quant_x1 = "<bin_{}>".format(int((boxes_target["boxes"][0][2] * (self.num_bins - 1)).round()))
306
+ quant_y1 = "<bin_{}>".format(int((boxes_target["boxes"][0][3] * (self.num_bins - 1)).round()))
307
+ region_coord = "{} {} {} {}".format(quant_x0, quant_y0, quant_x1, quant_y1)
308
+ src_caption = self.pre_caption(caption, self.max_src_length)
309
+ src_item = self.encode_text(' which region does the text " {} " describe?'.format(src_caption))
310
+ tgt_item = self.encode_text(region_coord, use_bpe=False)
311
+ else:
312
+ logger.info('type {} is not implemented'.format(type))
313
+ raise NotImplementedError
314
+
315
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
316
+ target_item = torch.cat([tgt_item, self.eos_item])
317
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
318
+ pos_src_item = torch.cat([self.bos_item, pos_src_item, self.eos_item]) if type != 'visual_grounding' else None
319
+ neg_src_item = torch.cat([self.bos_item, neg_src_item, self.eos_item]) if type != 'visual_grounding' else None
320
+
321
+ if type == 'caption' and dataset_name == 'cc12m':
322
+ target_item[:2] = self.src_dict.pad()
323
+ target_item[-1] = self.eos_item
324
+
325
+ example = {
326
+ "id": uniq_id,
327
+ "source": src_item,
328
+ "patch_image": patch_image,
329
+ "patch_mask": patch_mask,
330
+ "target": target_item,
331
+ "prev_output_tokens": prev_output_item,
332
+ "conf": conf,
333
+ }
334
+
335
+ examples = [example]
336
+ prob = random.random()
337
+ if type == 'visual_grounding':
338
+ region_example = example.copy()
339
+ region_prefix_item = self.encode_text(' what does the region describe? region:')
340
+ region_coord_item = self.encode_text('{}'.format(region_coord), use_bpe=False)
341
+ region_src_item = torch.cat([region_prefix_item, region_coord_item])
342
+ region_tgt_item = self.encode_text(' {}'.format(self.pre_caption(caption, self.max_tgt_length)))
343
+ region_example["source"] = torch.cat([self.bos_item, region_src_item, self.eos_item])
344
+ region_example["target"] = torch.cat([region_tgt_item, self.eos_item])
345
+ region_example["prev_output_tokens"] = torch.cat([self.bos_item, region_tgt_item])
346
+ region_example["conf"] = torch.tensor([1.0])
347
+ examples.append(region_example)
348
+ elif prob >= 0.5 and self.split == 'train':
349
+ pos_example = example.copy()
350
+ pos_example["source"] = pos_src_item
351
+ pos_example["target"] = torch.cat([self.pos_tgt_item, self.eos_item])
352
+ pos_example["prev_output_tokens"] = torch.cat([self.bos_item, self.pos_tgt_item])
353
+ examples.append(pos_example)
354
+ elif self.split == 'train':
355
+ neg_example = example.copy()
356
+ neg_example["source"] = neg_src_item
357
+ neg_example["target"] = torch.cat([self.neg_tgt_item, self.eos_item])
358
+ neg_example["prev_output_tokens"] = torch.cat([self.bos_item, self.neg_tgt_item])
359
+ examples.append(neg_example)
360
+ return examples
361
+
362
+ def process_pure_text(self, index):
363
+ patch_image = torch.zeros((3, self.code_image_size*2, self.code_image_size*2))
364
+ patch_mask = torch.tensor([False])
365
+ code_mask = torch.tensor([False])
366
+ conf = torch.tensor([2.0])
367
+
368
+ examples = []
369
+ for _ in range(2):
370
+ uniq_id, text = self.pure_text_dataset[index]
371
+ text = text.strip().lower()
372
+ text_item = self.encode_text(" {}".format(text), length=512)
373
+ text_item = text_item[-256:]
374
+ text_item = torch.cat([self.bos_item, text_item, self.eos_item])
375
+ mask_text_item = self.add_whole_word_mask(text_item.clone(), self.mask_ratio)
376
+ prefix_item = self.encode_text(' what is the complete text of " "?')
377
+ src_item = torch.cat([prefix_item[:-2], mask_text_item[1:-1], prefix_item[-2:]])
378
+ tgt_item = text_item[1:-1]
379
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
380
+ target_item = torch.cat([tgt_item, self.eos_item])
381
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
382
+ example = {
383
+ "id": uniq_id,
384
+ "source": src_item,
385
+ "patch_image": patch_image,
386
+ "patch_mask": patch_mask,
387
+ "code_mask": code_mask,
388
+ "target": target_item,
389
+ "prev_output_tokens": prev_output_item,
390
+ "conf": conf,
391
+ }
392
+ examples.append(example)
393
+
394
+ return examples
395
+
396
+ def process_pure_image(self, index):
397
+ image_id, image, code = self.pure_image_dataset[index]
398
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image))).convert("RGB")
399
+ patch_image = self.patch_crop_transform(image)
400
+ patch_image[:, self.mask_top:self.mask_bottom, self.mask_left:self.mask_right] = 0
401
+ patch_mask = torch.tensor([True])
402
+ src_item = self.encode_text(" what is the image in the middle part?")
403
+ image_code = torch.LongTensor([int(num) for num in code.strip().split()])
404
+ tgt_item = image_code + len(self.src_dict) - self.code_dict_size - self.num_bins
405
+ code_mask = torch.tensor([True])
406
+ conf = torch.tensor([2.0])
407
+
408
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
409
+ target_item = torch.cat([tgt_item, self.eos_item])
410
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
411
+
412
+ example = {
413
+ "id": image_id,
414
+ "source": src_item,
415
+ "patch_image": patch_image,
416
+ "patch_mask": patch_mask,
417
+ "code_mask": code_mask,
418
+ "target": target_item,
419
+ "prev_output_tokens": prev_output_item,
420
+ "conf": conf,
421
+ }
422
+ return [example]
423
+
424
+ def process_detection(self, index):
425
+ image_id, image, label = self.detection_dataset[index]
426
+ image = Image.open(BytesIO(base64.urlsafe_b64decode(image))).convert("RGB")
427
+
428
+ w, h = image.size
429
+ boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
430
+ label_list = label.strip().split('&&')
431
+ for label in label_list:
432
+ x0, y0, x1, y1, cat_id, cat = label.strip().split(',', 5)
433
+ boxes_target["boxes"].append([float(x0), float(y0), float(x1), float(y1)])
434
+ boxes_target["labels"].append(cat)
435
+ boxes_target["area"].append((float(x1) - float(x0)) * (float(y1) - float(y0)))
436
+ boxes_target["boxes"] = torch.tensor(boxes_target["boxes"])
437
+ boxes_target["labels"] = np.array(boxes_target["labels"])
438
+ boxes_target["area"] = torch.tensor(boxes_target["area"])
439
+
440
+ patch_image, boxes_target = self.detection_transform(image, boxes_target)
441
+ patch_mask = torch.tensor([True])
442
+ code_mask = torch.tensor([False])
443
+ conf = torch.tensor([2.0])
444
+
445
+ quant_boxes = []
446
+ for i, box in enumerate(boxes_target["boxes"]):
447
+ quant_boxes.extend(["<bin_{}>".format(int((pos * (self.num_bins - 1)).round())) for pos in box[:4]])
448
+ quant_boxes.append(self.bpe.encode(' {}'.format(boxes_target["labels"][i])))
449
+ src_item = self.encode_text(' what are the objects in the image?')
450
+ tgt_item = self.encode_text(' '.join(quant_boxes), use_bpe=False)
451
+
452
+ src_item = torch.cat([self.bos_item, src_item, self.eos_item])
453
+ target_item = torch.cat([tgt_item, self.eos_item])
454
+ prev_output_item = torch.cat([self.bos_item, tgt_item])
455
+
456
+ example = {
457
+ "id": image_id,
458
+ "source": src_item,
459
+ "patch_image": patch_image,
460
+ "patch_mask": patch_mask,
461
+ "code_mask": code_mask,
462
+ "target": target_item,
463
+ "prev_output_tokens": prev_output_item,
464
+ "conf": conf,
465
+ }
466
+ return [example]
467
+
468
+ def __getitem__(self, index):
469
+ with data_utils.numpy_seed(self.seed, self.epoch):
470
+ pair_samples = self.process_image_text_pair(index)
471
+ extra_samples = []
472
+ if self.split == 'train' and self.dataset.data_cnt % 8 == 0:
473
+ extra_samples += self.process_pure_text(0) if self.pure_text_dataset else []
474
+ extra_samples += self.process_pure_image(0) if self.pure_image_dataset else []
475
+ extra_samples += self.process_detection(0) if self.detection_dataset else []
476
+ return pair_samples, extra_samples
477
+
478
+ def word_starts(self, source):
479
+ if self.mask_whole_word is not None:
480
+ is_word_start = self.mask_whole_word.gather(0, source)
481
+ else:
482
+ is_word_start = torch.ones(source.size())
483
+ is_word_start[0] = 0
484
+ is_word_start[-1] = 0
485
+ return is_word_start
486
+
487
+ def add_whole_word_mask(self, source, p):
488
+ is_word_start = self.word_starts(source)
489
+ num_to_mask = int(math.ceil(is_word_start.float().sum() * p))
490
+ num_inserts = 0
491
+ if num_to_mask == 0:
492
+ return source
493
+
494
+ if self.mask_span_distribution is not None:
495
+ lengths = self.mask_span_distribution.sample(sample_shape=(num_to_mask,))
496
+
497
+ # Make sure we have enough to mask
498
+ cum_length = torch.cumsum(lengths, 0)
499
+ while cum_length[-1] < num_to_mask:
500
+ lengths = torch.cat(
501
+ [
502
+ lengths,
503
+ self.mask_span_distribution.sample(sample_shape=(num_to_mask,)),
504
+ ],
505
+ dim=0,
506
+ )
507
+ cum_length = torch.cumsum(lengths, 0)
508
+
509
+ # Trim to masking budget
510
+ i = 0
511
+ while cum_length[i] < num_to_mask:
512
+ i += 1
513
+ lengths[i] = num_to_mask - (0 if i == 0 else cum_length[i - 1])
514
+ num_to_mask = i + 1
515
+ lengths = lengths[:num_to_mask]
516
+
517
+ # Handle 0-length mask (inserts) separately
518
+ lengths = lengths[lengths > 0]
519
+ num_inserts = num_to_mask - lengths.size(0)
520
+ num_to_mask -= num_inserts
521
+ if num_to_mask == 0:
522
+ return self.add_insertion_noise(source, num_inserts / source.size(0))
523
+
524
+ assert (lengths > 0).all()
525
+ else:
526
+ lengths = torch.ones((num_to_mask,)).long()
527
+ assert is_word_start[-1] == 0
528
+ word_starts = is_word_start.nonzero(as_tuple=False)
529
+ indices = word_starts[
530
+ torch.randperm(word_starts.size(0))[:num_to_mask]
531
+ ].squeeze(1)
532
+ mask_random = torch.FloatTensor(num_to_mask).uniform_() < self.random_ratio
533
+
534
+ source_length = source.size(0)
535
+ assert source_length - 1 not in indices
536
+ to_keep = torch.ones(source_length, dtype=torch.bool)
537
+ is_word_start[
538
+ -1
539
+ ] = 255 # acts as a long length, so spans don't go over the end of doc
540
+ if self.replace_length == 0:
541
+ to_keep[indices] = 0
542
+ else:
543
+ # keep index, but replace it with [MASK]
544
+ source[indices] = self.mask_idx
545
+ source[indices[mask_random]] = torch.randint(
546
+ 4, len(self.tgt_dict) - self.code_dict_size - self.num_bins, size=(mask_random.sum(),)
547
+ )
548
+
549
+ if self.mask_span_distribution is not None:
550
+ assert len(lengths.size()) == 1
551
+ assert lengths.size() == indices.size()
552
+ lengths -= 1
553
+ while indices.size(0) > 0:
554
+ assert lengths.size() == indices.size()
555
+ lengths -= is_word_start[indices + 1].long()
556
+ uncompleted = lengths >= 0
557
+ indices = indices[uncompleted] + 1
558
+ mask_random = mask_random[uncompleted]
559
+ lengths = lengths[uncompleted]
560
+ if self.replace_length != -1:
561
+ # delete token
562
+ to_keep[indices] = 0
563
+ else:
564
+ # keep index, but replace it with [MASK]
565
+ source[indices] = self.mask_idx
566
+ source[indices[mask_random]] = torch.randint(
567
+ 4, len(self.tgt_dict) - self.code_dict_size - self.num_bins, size=(mask_random.sum(),)
568
+ )
569
+ else:
570
+ # A bit faster when all lengths are 1
571
+ while indices.size(0) > 0:
572
+ uncompleted = is_word_start[indices + 1] == 0
573
+ indices = indices[uncompleted] + 1
574
+ mask_random = mask_random[uncompleted]
575
+ if self.replace_length != -1:
576
+ # delete token
577
+ to_keep[indices] = 0
578
+ else:
579
+ # keep index, but replace it with [MASK]
580
+ source[indices] = self.mask_idx
581
+ source[indices[mask_random]] = torch.randint(
582
+ 4, len(self.tgt_dict) - self.code_dict_size - self.num_bins, size=(mask_random.sum(),)
583
+ )
584
+
585
+ assert source_length - 1 not in indices
586
+
587
+ source = source[to_keep]
588
+
589
+ if num_inserts > 0:
590
+ source = self.add_insertion_noise(source, num_inserts / source.size(0))
591
+
592
+ return source
593
+
594
+ def add_insertion_noise(self, tokens, p):
595
+ if p == 0.0:
596
+ return tokens
597
+
598
+ num_tokens = len(tokens)
599
+ n = int(math.ceil(num_tokens * p))
600
+
601
+ noise_indices = torch.randperm(num_tokens + n - 2)[:n] + 1
602
+ noise_mask = torch.zeros(size=(num_tokens + n,), dtype=torch.bool)
603
+ noise_mask[noise_indices] = 1
604
+ result = torch.LongTensor(n + len(tokens)).fill_(-1)
605
+
606
+ num_random = int(math.ceil(n * self.random_ratio))
607
+ result[noise_indices[num_random:]] = self.mask_idx
608
+ result[noise_indices[:num_random]] = torch.randint(
609
+ low=4, high=len(self.tgt_dict)-self.code_dict_size-self.num_bins, size=(num_random,)
610
+ )
611
+
612
+ result[~noise_mask] = tokens
613
+
614
+ assert (result >= 0).all()
615
+ return result
616
+
617
+ def collater(self, samples, pad_to_length=None):
618
+ """Merge samples of different tasks to form two mini-batches.
619
+ Args:
620
+ samples (List[Tuple]): samples to collate
621
+ Returns:
622
+ Tuple[dict]: two mini-batch containing the data of different tasks
623
+ """
624
+
625
+ samples_v1 = [] # containing image-text pairs
626
+ samples_v2 = [] # containing detection data, text data and image data
627
+ for sample_tuple in samples:
628
+ samples_v1 += sample_tuple[0]
629
+ samples_v2 += sample_tuple[1]
630
+ if samples_v2 != []:
631
+ res_v1 = collate(samples_v1, pad_idx=self.src_dict.pad(), eos_idx=self.eos)
632
+ res_v2 = collate(samples_v2, pad_idx=self.src_dict.pad(), eos_idx=self.eos)
633
+ return res_v1, res_v2
634
+ else:
635
+ res_v1 = collate(samples_v1, pad_idx=self.src_dict.pad(), eos_idx=self.eos)
636
+ return res_v1
datasets.md ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Datasets
2
+
3
+ We provide links to download our preprocessed dataset. If you would like to process the data on your own, we will soon provide scripts for you to do so.
4
+
5
+ ## Pretraining
6
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/pretrain_data/pretrain_data_examples.zip"> A small subset of the pretraining data </a>
7
+
8
+ The pretraining datasets used in OFA are all publicly available. Here we provide the public links to these data, it is recommended that you download the data from the links first, and then process the downloaded dataset into a similar format as the examples we provided.
9
+ - _CC12M_: https://github.com/google-research-datasets/conceptual-12m
10
+ - _CC3M_: https://github.com/google-research-datasets/conceptual-captions
11
+ - _SBU_: https://www.cs.virginia.edu/~vicente/sbucaptions
12
+ - _COCO_: https://cocodataset.org/#home
13
+ - _VG_: https://visualgenome.org/
14
+ - _VQAv2_: https://visualqa.org/
15
+ - _GQA_: https://cs.stanford.edu/people/dorarad/gqa/about.html
16
+ - _RefCOCO_/_RefCOCO+_/RefCOCOg: https://github.com/lichengunc/refer
17
+ - _OpenImages_: https://storage.googleapis.com/openimages/web/index.html
18
+ - _Object365_: https://www.objects365.org/overview.html
19
+ - _YFCC100M (subset)_: https://github.com/openai/CLIP/blob/main/data/yfcc100m.md
20
+ - _ImageNet-21K_: https://image-net.org/index.php
21
+ - _Pile_: https://pile.eleuther.ai
22
+
23
+ ## Vision & Language Tasks
24
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/caption_data/caption_data.zip"> Dataset for Caption </a>
25
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/refcoco_data/refcoco_data.zip"> Dataset for RefCOCO </a>
26
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/refcocoplus_data/refcocoplus_data.zip"> Dataset for RefCOCO+ </a>
27
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/refcocog_data/refcocog_data.zip"> Dataset for RefCOCOg </a>
28
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/vqa_data/vqa_data.zip"> Dataset for VQAv2 </a> (we have also provided chunked parts of the dataset files for more convenient downloading, please refer to <a href="https://github.com/OFA-Sys/OFA/issues/68#issuecomment-1096837349">issue #68</a>)
29
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/snli_ve_data/snli_ve_data.zip"> Dataset for SNLI-VE </a>
30
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/coco_image_gen_data/coco_image_gen.zip"> Dataset for Text-to-Image Genearion </a>
31
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/coco_image_gen_data/coco_image_gen_origin_id.zip"> Dataset for Text-to-Image Genearion (with original id) </a>
32
+
33
+ ## Vision Tasks
34
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/imagenet_1k_data/imagenet_1k_data.zip"> Dataset for ImageNet-1K </a>
35
+
36
+ ## Language Tasks
37
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/cola_data.zip"> Dataset for COLA </a>
38
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/mnli_data.zip"> Dataset for MNLI </a>
39
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/mrpc_data.zip"> Dataset for MRPC </a>
40
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/qnli_data.zip"> Dataset for QNLI </a>
41
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/qqp_data.zip"> Dataset for QQP </a>
42
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/rte_data.zip"> Dataset for RTE </a>
43
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/sst2_data.zip"> Dataset for SST2 </a>
44
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/gigaword_data/gigaword_data.zip"> Dataset for Gigaword </a>
evaluate.py ADDED
@@ -0,0 +1,160 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3 -u
2
+ # Copyright 2022 The OFA-Sys Team.
3
+ # All rights reserved.
4
+ # This source code is licensed under the Apache 2.0 license
5
+ # found in the LICENSE file in the root directory.
6
+
7
+ import logging
8
+ import os
9
+ import sys
10
+
11
+ import numpy as np
12
+ import torch
13
+ from fairseq import distributed_utils, options, tasks, utils
14
+ from fairseq.dataclass.utils import convert_namespace_to_omegaconf
15
+ from fairseq.logging import progress_bar
16
+ from fairseq.utils import reset_logging
17
+ from omegaconf import DictConfig
18
+
19
+ from utils import checkpoint_utils
20
+ from utils.eval_utils import eval_step, merge_results
21
+ from utils.zero_shot_utils import zero_shot_step
22
+
23
+ logging.basicConfig(
24
+ format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
25
+ datefmt="%Y-%m-%d %H:%M:%S",
26
+ level=os.environ.get("LOGLEVEL", "INFO").upper(),
27
+ stream=sys.stdout,
28
+ )
29
+ logger = logging.getLogger("ofa.evaluate")
30
+
31
+
32
+ def apply_half(t):
33
+ if t.dtype is torch.float32:
34
+ return t.to(dtype=torch.half)
35
+ return t
36
+
37
+
38
+ def main(cfg: DictConfig, **kwargs):
39
+ utils.import_user_module(cfg.common)
40
+
41
+ reset_logging()
42
+ logger.info(cfg)
43
+
44
+ assert (
45
+ cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
46
+ ), "Must specify batch size either with --max-tokens or --batch-size"
47
+
48
+ # Fix seed for stochastic decoding
49
+ if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
50
+ np.random.seed(cfg.common.seed)
51
+ utils.set_torch_seed(cfg.common.seed)
52
+
53
+ use_fp16 = cfg.common.fp16
54
+ use_cuda = torch.cuda.is_available() and not cfg.common.cpu
55
+
56
+ if use_cuda:
57
+ torch.cuda.set_device(cfg.distributed_training.device_id)
58
+
59
+ # Load ensemble
60
+ overrides = eval(cfg.common_eval.model_overrides)
61
+ # Deal with beam-search / all-candidate VQA eval
62
+ if cfg.task._name == "vqa_gen":
63
+ overrides['val_inference_type'] = "beamsearch" if kwargs['beam_search_vqa_eval'] else "allcand"
64
+
65
+ logger.info("loading model(s) from {}".format(cfg.common_eval.path))
66
+ if kwargs["zero_shot"]:
67
+ task = tasks.setup_task(cfg.task)
68
+ models, saved_cfg = checkpoint_utils.load_model_ensemble(
69
+ utils.split_paths(cfg.common_eval.path),
70
+ arg_overrides=overrides,
71
+ task=task,
72
+ suffix=cfg.checkpoint.checkpoint_suffix,
73
+ strict=(cfg.checkpoint.checkpoint_shard_count == 1),
74
+ num_shards=cfg.checkpoint.checkpoint_shard_count,
75
+ )
76
+ else:
77
+ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
78
+ utils.split_paths(cfg.common_eval.path),
79
+ arg_overrides=overrides,
80
+ suffix=cfg.checkpoint.checkpoint_suffix,
81
+ strict=(cfg.checkpoint.checkpoint_shard_count == 1),
82
+ num_shards=cfg.checkpoint.checkpoint_shard_count,
83
+ )
84
+
85
+ # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
86
+ task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
87
+
88
+ # Move models to GPU
89
+ for model, ckpt_path in zip(models, utils.split_paths(cfg.common_eval.path)):
90
+ if kwargs['ema_eval']:
91
+ logger.info("loading EMA weights from {}".format(ckpt_path))
92
+ model.load_state_dict(checkpoint_utils.load_ema_from_checkpoint(ckpt_path)['model'])
93
+ model.eval()
94
+ if use_fp16:
95
+ model.half()
96
+ if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
97
+ model.cuda()
98
+ model.prepare_for_inference_(cfg)
99
+
100
+ # Load dataset (possibly sharded)
101
+ itr = task.get_batch_iterator(
102
+ dataset=task.dataset(cfg.dataset.gen_subset),
103
+ max_tokens=cfg.dataset.max_tokens,
104
+ max_sentences=cfg.dataset.batch_size,
105
+ max_positions=utils.resolve_max_positions(
106
+ task.max_positions(), *[m.max_positions() for m in models]
107
+ ),
108
+ ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
109
+ required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
110
+ seed=cfg.common.seed,
111
+ num_shards=cfg.distributed_training.distributed_world_size,
112
+ shard_id=cfg.distributed_training.distributed_rank,
113
+ num_workers=cfg.dataset.num_workers,
114
+ data_buffer_size=cfg.dataset.data_buffer_size,
115
+ ).next_epoch_itr(shuffle=False)
116
+ progress = progress_bar.progress_bar(
117
+ itr,
118
+ log_format=cfg.common.log_format,
119
+ log_interval=cfg.common.log_interval,
120
+ default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
121
+ )
122
+
123
+ # Initialize generator
124
+ generator = task.build_generator(models, cfg.generation)
125
+
126
+ results = []
127
+ score_sum = torch.FloatTensor([0]).cuda()
128
+ score_cnt = torch.FloatTensor([0]).cuda()
129
+ for sample in progress:
130
+ if "net_input" not in sample:
131
+ continue
132
+ sample = utils.move_to_cuda(sample) if use_cuda else sample
133
+ sample = utils.apply_to_sample(apply_half, sample) if cfg.common.fp16 else sample
134
+ with torch.no_grad():
135
+ if kwargs["zero_shot"]:
136
+ result, scores = zero_shot_step(task, generator, models, sample)
137
+ else:
138
+ result, scores = eval_step(task, generator, models, sample, **kwargs)
139
+ results += result
140
+ score_sum += sum(scores) if scores is not None else 0
141
+ score_cnt += len(scores) if scores is not None else 0
142
+ progress.log({"sentences": sample["nsentences"]})
143
+
144
+ merge_results(task, cfg, logger, score_cnt, score_sum, results)
145
+
146
+
147
+ def cli_main():
148
+ parser = options.get_generation_parser()
149
+ parser.add_argument("--ema-eval", action='store_true', help="Use EMA weights to make evaluation.")
150
+ parser.add_argument("--beam-search-vqa-eval", action='store_true', help="Use beam search for vqa evaluation (faster inference speed but sub-optimal result), if not specified, we compute scores for each answer in the candidate set, which is slower but can obtain best result.")
151
+ parser.add_argument("--zero-shot", action='store_true')
152
+ args = options.parse_args_and_arch(parser)
153
+ cfg = convert_namespace_to_omegaconf(args)
154
+ distributed_utils.call_main(
155
+ cfg, main, ema_eval=args.ema_eval, beam_search_vqa_eval=args.beam_search_vqa_eval, zero_shot=args.zero_shot
156
+ )
157
+
158
+
159
+ if __name__ == "__main__":
160
+ cli_main()
fairseq/.github/ISSUE_TEMPLATE.md ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ ## 👉 [Please follow one of these issue templates](https://github.com/pytorch/fairseq/issues/new/choose) 👈
2
+
3
+ Note: to keep the backlog clean and actionable, issues may be immediately closed if they do not follow one of the above issue templates.
fairseq/.github/ISSUE_TEMPLATE/bug_report.md ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ name: 🐛 Bug Report
3
+ about: Submit a bug report to help us improve
4
+ labels: 'bug, needs triage'
5
+ ---
6
+
7
+ ## 🐛 Bug
8
+
9
+ <!-- A clear and concise description of what the bug is. -->
10
+
11
+ ### To Reproduce
12
+
13
+ Steps to reproduce the behavior (**always include the command you ran**):
14
+
15
+ 1. Run cmd '....'
16
+ 2. See error
17
+
18
+ <!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
19
+
20
+
21
+ #### Code sample
22
+ <!-- Ideally attach a minimal code sample to reproduce the decried issue.
23
+ Minimal means having the shortest code but still preserving the bug. -->
24
+
25
+ ### Expected behavior
26
+
27
+ <!-- A clear and concise description of what you expected to happen. -->
28
+
29
+ ### Environment
30
+
31
+ - fairseq Version (e.g., 1.0 or main):
32
+ - PyTorch Version (e.g., 1.0)
33
+ - OS (e.g., Linux):
34
+ - How you installed fairseq (`pip`, source):
35
+ - Build command you used (if compiling from source):
36
+ - Python version:
37
+ - CUDA/cuDNN version:
38
+ - GPU models and configuration:
39
+ - Any other relevant information:
40
+
41
+ ### Additional context
42
+
43
+ <!-- Add any other context about the problem here. -->
fairseq/.github/ISSUE_TEMPLATE/documentation.md ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ name: 📚 Documentation/Typos
3
+ about: Report an issue related to documentation or a typo
4
+ labels: 'documentation, needs triage'
5
+ ---
6
+
7
+ ## 📚 Documentation
8
+
9
+ For typos and doc fixes, please go ahead and:
10
+
11
+ 1. Create an issue.
12
+ 2. Fix the typo.
13
+ 3. Submit a PR.
14
+
15
+ Thanks!
fairseq/.github/ISSUE_TEMPLATE/feature_request.md ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ name: 🚀 Feature Request
3
+ about: Submit a proposal/request for a new feature
4
+ labels: 'enhancement, help wanted, needs triage'
5
+ ---
6
+
7
+ ## 🚀 Feature Request
8
+ <!-- A clear and concise description of the feature proposal -->
9
+
10
+ ### Motivation
11
+
12
+ <!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
13
+
14
+ ### Pitch
15
+
16
+ <!-- A clear and concise description of what you want to happen. -->
17
+
18
+ ### Alternatives
19
+
20
+ <!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
21
+
22
+ ### Additional context
23
+
24
+ <!-- Add any other context or screenshots about the feature request here. -->
fairseq/.github/ISSUE_TEMPLATE/how-to-question.md ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ name: ❓ Questions/Help
3
+ about: If you have questions, please first search existing issues and docs
4
+ labels: 'question, needs triage'
5
+ ---
6
+
7
+ ## ❓ Questions and Help
8
+
9
+ ### Before asking:
10
+ 1. search the issues.
11
+ 2. search the docs.
12
+
13
+ <!-- If you still can't find what you need: -->
14
+
15
+ #### What is your question?
16
+
17
+ #### Code
18
+
19
+ <!-- Please paste a code snippet if your question requires it! -->
20
+
21
+ #### What have you tried?
22
+
23
+ #### What's your environment?
24
+
25
+ - fairseq Version (e.g., 1.0 or main):
26
+ - PyTorch Version (e.g., 1.0)
27
+ - OS (e.g., Linux):
28
+ - How you installed fairseq (`pip`, source):
29
+ - Build command you used (if compiling from source):
30
+ - Python version:
31
+ - CUDA/cuDNN version:
32
+ - GPU models and configuration:
33
+ - Any other relevant information:
fairseq/.github/PULL_REQUEST_TEMPLATE.md ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Before submitting
2
+
3
+ - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
4
+ - [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)?
5
+ - [ ] Did you make sure to update the docs?
6
+ - [ ] Did you write any new necessary tests?
7
+
8
+ ## What does this PR do?
9
+ Fixes # (issue).
10
+
11
+ ## PR review
12
+ Anyone in the community is free to review the PR once the tests have passed.
13
+ If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
14
+
15
+ ## Did you have fun?
16
+ Make sure you had fun coding 🙃
fairseq/.github/stale.yml ADDED
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1
+ # Configuration for probot-stale - https://github.com/probot/stale
2
+ # Mostly copied from github.com/facebook/react/blob/master/.github/stale.yml
3
+ # Number of days of inactivity before an issue becomes stale
4
+ daysUntilStale: 90
5
+ # Number of days of inactivity before a stale issue is closed
6
+ daysUntilClose: 7
7
+ # Issues with these labels will never be considered stale
8
+ exemptLabels:
9
+ - bug
10
+ # Label to use when marking an issue as stale
11
+ staleLabel: stale
12
+ issues:
13
+ # Comment to post when marking an issue as stale.
14
+ markComment: >
15
+ This issue has been automatically marked as stale.
16
+ **If this issue is still affecting you, please leave any comment** (for example, "bump"), and we'll keep it open.
17
+ We are sorry that we haven't been able to prioritize it yet. If you have any new additional information, please include it with your comment!
18
+ # Comment to post when closing a stale issue.
19
+ closeComment: >
20
+ Closing this issue after a prolonged period of inactivity. If this issue is still present in the latest release, please create a new issue with up-to-date information. Thank you!
21
+ pulls:
22
+ # Comment to post when marking a pull request as stale.
23
+ markComment: >
24
+ This pull request has been automatically marked as stale.
25
+ **If this pull request is still relevant, please leave any comment** (for example, "bump"), and we'll keep it open.
26
+ We are sorry that we haven't been able to prioritize reviewing it yet. Your contribution is very much appreciated.
27
+ # Comment to post when closing a stale pull request.
28
+ closeComment: >
29
+ Closing this pull request after a prolonged period of inactivity. If this issue is still present in the latest release, please ask for this pull request to be reopened. Thank you!
30
+
fairseq/.github/workflows/build.yml ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: build
2
+
3
+ on:
4
+ # Trigger the workflow on push to main or any pull request
5
+ push:
6
+ branches:
7
+ - main
8
+ pull_request:
9
+
10
+ jobs:
11
+ build:
12
+
13
+ strategy:
14
+ max-parallel: 4
15
+ matrix:
16
+ platform: [ubuntu-latest, macos-latest]
17
+ python-version: [3.6, 3.7]
18
+
19
+ runs-on: ${{ matrix.platform }}
20
+
21
+ steps:
22
+ - uses: actions/checkout@v2
23
+
24
+ - name: Set up Python ${{ matrix.python-version }}
25
+ uses: actions/setup-python@v2
26
+ with:
27
+ python-version: ${{ matrix.python-version }}
28
+
29
+ - name: Conditionally install pytorch
30
+ if: matrix.platform == 'windows-latest'
31
+ run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html
32
+
33
+ - name: Install locally
34
+ run: |
35
+ python -m pip install --upgrade pip
36
+ git submodule update --init --recursive
37
+ python setup.py build_ext --inplace
38
+ python -m pip install --editable .
39
+
40
+ - name: Install optional test requirements
41
+ run: |
42
+ python -m pip install iopath transformers pyarrow
43
+ python -m pip install git+https://github.com/facebookresearch/fairscale.git@main
44
+
45
+ - name: Lint with flake8
46
+ run: |
47
+ pip install flake8
48
+ # stop the build if there are Python syntax errors or undefined names
49
+ flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --extend-exclude fairseq/model_parallel/megatron
50
+ # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
51
+ flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --extend-exclude fairseq/model_parallel/megatron
52
+
53
+ - name: Run tests
54
+ run: |
55
+ python setup.py test
fairseq/.github/workflows/build_wheels.yml ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: build_wheels
2
+
3
+ on:
4
+ push:
5
+ branches:
6
+ - v[0-9]+.[0-9]+.[x0-9]+
7
+ tags:
8
+ - v*
9
+
10
+ jobs:
11
+ build_wheels:
12
+ name: Build wheels on ${{ matrix.os }}
13
+ runs-on: ${{ matrix.os }}
14
+ strategy:
15
+ matrix:
16
+ os: [ubuntu-latest, macos-latest]
17
+
18
+ steps:
19
+ - uses: actions/checkout@v2
20
+
21
+ - name: Install Python
22
+ uses: actions/setup-python@v2
23
+ with:
24
+ python-version: '3.7'
25
+
26
+ - name: Install cibuildwheel
27
+ run: |
28
+ python -m pip install cibuildwheel
29
+
30
+ - name: Build wheels for CPython
31
+ run: |
32
+ python -m cibuildwheel --output-dir dist
33
+ env:
34
+ CIBW_BUILD: "cp36-*64 cp37-*64 cp38-*64"
35
+ CIBW_MANYLINUX_X86_64_IMAGE: manylinux1
36
+ CIBW_BEFORE_BUILD: git submodule update --init --recursive && pip install .
37
+
38
+ - uses: actions/upload-artifact@v2
39
+ with:
40
+ name: wheels
41
+ path: ./dist/*.whl
fairseq/.gitignore ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # JetBrains PyCharm IDE
2
+ .idea/
3
+
4
+ # Byte-compiled / optimized / DLL files
5
+ __pycache__/
6
+ *.py[cod]
7
+ *$py.class
8
+
9
+ # C extensions
10
+ *.so
11
+
12
+ # macOS dir files
13
+ .DS_Store
14
+
15
+ # Distribution / packaging
16
+ .Python
17
+ env/
18
+ build/
19
+ develop-eggs/
20
+ dist/
21
+ downloads/
22
+ eggs/
23
+ .eggs/
24
+ lib/
25
+ lib64/
26
+ parts/
27
+ sdist/
28
+ var/
29
+ wheels/
30
+ *.egg-info/
31
+ .installed.cfg
32
+ *.egg
33
+
34
+ # Checkpoints
35
+ checkpoints
36
+
37
+ # PyInstaller
38
+ # Usually these files are written by a python script from a template
39
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
40
+ *.manifest
41
+ *.spec
42
+
43
+ # Installer logs
44
+ pip-log.txt
45
+ pip-delete-this-directory.txt
46
+
47
+ # Unit test / coverage reports
48
+ htmlcov/
49
+ .tox/
50
+ .coverage
51
+ .coverage.*
52
+ .cache
53
+ nosetests.xml
54
+ coverage.xml
55
+ *.cover
56
+ .hypothesis/
57
+
58
+ # Translations
59
+ *.mo
60
+ *.pot
61
+
62
+ # Django stuff:
63
+ *.log
64
+ local_settings.py
65
+
66
+ # Flask stuff:
67
+ instance/
68
+ .webassets-cache
69
+
70
+ # Scrapy stuff:
71
+ .scrapy
72
+
73
+ # Sphinx documentation
74
+ docs/_build/
75
+
76
+ # PyBuilder
77
+ target/
78
+
79
+ # Jupyter Notebook
80
+ .ipynb_checkpoints
81
+
82
+ # pyenv
83
+ .python-version
84
+
85
+ # celery beat schedule file
86
+ celerybeat-schedule
87
+
88
+ # SageMath parsed files
89
+ *.sage.py
90
+
91
+ # dotenv
92
+ .env
93
+
94
+ # virtualenv
95
+ .venv
96
+ venv/
97
+ ENV/
98
+
99
+ # Spyder project settings
100
+ .spyderproject
101
+ .spyproject
102
+
103
+ # Rope project settings
104
+ .ropeproject
105
+
106
+ # mkdocs documentation
107
+ /site
108
+
109
+ # mypy
110
+ .mypy_cache/
111
+
112
+ # Generated files
113
+ /fairseq/temporal_convolution_tbc
114
+ /fairseq/modules/*_layer/*_forward.cu
115
+ /fairseq/modules/*_layer/*_backward.cu
116
+ /fairseq/version.py
117
+
118
+ # data
119
+ data-bin/
120
+
121
+ # reranking
122
+ /examples/reranking/rerank_data
123
+
124
+ # Cython-generated C++ source files
125
+ /fairseq/data/data_utils_fast.cpp
126
+ /fairseq/data/token_block_utils_fast.cpp
127
+
128
+ # VSCODE
129
+ .vscode/ftp-sync.json
130
+ .vscode/settings.json
131
+
132
+ # Experimental Folder
133
+ experimental/*
134
+
135
+ # Weights and Biases logs
136
+ wandb/
fairseq/.gitmodules ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ [submodule "fairseq/model_parallel/megatron"]
2
+ path = fairseq/model_parallel/megatron
3
+ url = https://github.com/ngoyal2707/Megatron-LM
4
+ branch = fairseq
fairseq/CODE_OF_CONDUCT.md ADDED
@@ -0,0 +1,77 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Code of Conduct
2
+
3
+ ## Our Pledge
4
+
5
+ In the interest of fostering an open and welcoming environment, we as
6
+ contributors and maintainers pledge to make participation in our project and
7
+ our community a harassment-free experience for everyone, regardless of age, body
8
+ size, disability, ethnicity, sex characteristics, gender identity and expression,
9
+ level of experience, education, socio-economic status, nationality, personal
10
+ appearance, race, religion, or sexual identity and orientation.
11
+
12
+ ## Our Standards
13
+
14
+ Examples of behavior that contributes to creating a positive environment
15
+ include:
16
+
17
+ * Using welcoming and inclusive language
18
+ * Being respectful of differing viewpoints and experiences
19
+ * Gracefully accepting constructive criticism
20
+ * Focusing on what is best for the community
21
+ * Showing empathy towards other community members
22
+
23
+ Examples of unacceptable behavior by participants include:
24
+
25
+ * The use of sexualized language or imagery and unwelcome sexual attention or
26
+ advances
27
+ * Trolling, insulting/derogatory comments, and personal or political attacks
28
+ * Public or private harassment
29
+ * Publishing others' private information, such as a physical or electronic
30
+ address, without explicit permission
31
+ * Other conduct which could reasonably be considered inappropriate in a
32
+ professional setting
33
+
34
+ ## Our Responsibilities
35
+
36
+ Project maintainers are responsible for clarifying the standards of acceptable
37
+ behavior and are expected to take appropriate and fair corrective action in
38
+ response to any instances of unacceptable behavior.
39
+
40
+ Project maintainers have the right and responsibility to remove, edit, or
41
+ reject comments, commits, code, wiki edits, issues, and other contributions
42
+ that are not aligned to this Code of Conduct, or to ban temporarily or
43
+ permanently any contributor for other behaviors that they deem inappropriate,
44
+ threatening, offensive, or harmful.
45
+
46
+ ## Scope
47
+
48
+ This Code of Conduct applies within all project spaces, and it also applies when
49
+ an individual is representing the project or its community in public spaces.
50
+ Examples of representing a project or community include using an official
51
+ project e-mail address, posting via an official social media account, or acting
52
+ as an appointed representative at an online or offline event. Representation of
53
+ a project may be further defined and clarified by project maintainers.
54
+
55
+ ## Enforcement
56
+
57
+ Instances of abusive, harassing, or otherwise unacceptable behavior may be
58
+ reported by contacting the project team at <conduct@pytorch.org>. All
59
+ complaints will be reviewed and investigated and will result in a response that
60
+ is deemed necessary and appropriate to the circumstances. The project team is
61
+ obligated to maintain confidentiality with regard to the reporter of an incident.
62
+ Further details of specific enforcement policies may be posted separately.
63
+
64
+ Project maintainers who do not follow or enforce the Code of Conduct in good
65
+ faith may face temporary or permanent repercussions as determined by other
66
+ members of the project's leadership.
67
+
68
+ ## Attribution
69
+
70
+ This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
71
+ available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
72
+
73
+ [homepage]: https://www.contributor-covenant.org
74
+
75
+ For answers to common questions about this code of conduct, see
76
+ https://www.contributor-covenant.org/faq
77
+