CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

Related tags

Deep Learningtext-generationchinesepretrained-modelsptmslanguage-understandingtransformer-architecture
Overview

CPT

This repository contains code and checkpoints for CPT.

CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, Xipeng Qiu

Introduction

Aiming to unify both NLU and NLG tasks, We propose a novel Chinese Pre-trained Un-balanced Transformer (CPT), which is an unbalanced Transformer encoder-decoder pre-trained with MLM and DAE jointly.



The architecture of CPT is a variant of the full Transformer and consists of three parts:

  1. Shared Encoder (S-Enc): a Transformer encoder with fully-connected self-attention, which is designed to capture the common semantic representation for both language understanding and generation.
  2. Understanding Decoder (U-Dec): a shallow Transformer encoder with fully-connected self-attention, which is designed for NLU tasks. The input of U-Dec is the output of S-Enc.
  3. Generation Decoder (G-Dec): a Transformer decoder with masked self-attention, which is designed for generation tasks with auto-regressive fashion. G-Dec utilizes the output of S-Enc with cross-attention.

Downloads & Usage

Coming soon.

Chinese BART

We also provide a pre-trained Chinese BART as a byproduct. The BART models is pre-trained with the same corpora, tokenization and hyper-parameters of CPT.

Load with Huggingface-Transformers

Chinese BART is available in base and large versions, and can be loaded with Huggingface-Transformers. The example code is as follows, where MODEL_NAME is fnlp/bart-base-chinese or fnlp/bart-large-chinese for base or large size of BART, respectively.

>>> tokenizer = BertTokenizer.from_pretrained("MODEL_NAME")
>>> model = BartForConditionalGeneration.from_pretrained("MODEL_NAME")

Citation

@article{shao2021cpt,
  title={CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation}, 
  author={Yunfan Shao and Zhichao Geng and Yitao Liu and Junqi Dai and Fei Yang and Li Zhe and Hujun Bao and Xipeng Qiu},
  journal={arXiv preprint arXiv:2109.05729},
  year={2021}
}
Comments
  • generation/LCSTS数据集上效果没达到

    generation/LCSTS数据集上效果没达到

    您好，我最近在LCSTS数据集上跑了您的代码，结果只有rouge-L：31，论文给的结果是38左右，差很多。

    数据集直接在网上下载然后处理成如下格式：

    {"summarization": "可穿戴技术十大设计原则", "article": "本文总结了十个可穿戴产品的设计原则，而这些原则，同样也是笔者认为是这个行业最吸引人的地方：1.为人们解决重复性问题；2.从人开始，而不是从机器开始；3.要引起注意，但不要刻意；4.提升用户能力，而不是取代人"}

    代码只修改了文件路径，其余无改动。 请问问题可能出在哪里呢？ run_gen.py中的默认超参数，是否是最优的超参数呢？

    opened by zhoucz97 17
  • 用huggingface代码直接进行BART large fineturning出现繁体字

    用huggingface代码直接进行BART large fineturning出现繁体字

    以下为训练集的数据，训练了1000epoch，可以看到不仅预算变成了預算（繁简），而且A=SM变成了a=sm（大小写），也就是连训练集都没有拟合，训练过程loss是接近于0的

    生成: 题目：《sm公司全面预算管理问题研究》，句式：a，其中a=sm公园公司的全面預算管辖问题探究 label: 题目：《SM 公司全面预算管理问题研究》，句式：A，其中A=SM 公司全面预算管理问题研究

    想问下可能的原因

    opened by yht4work 7
  • ner模型的问题

    ner模型的问题

    按照您提供的运行指令 python -m torch.distributed.launch --nproc_per_node 1 --nnodes 1
    train_msra.py
    --ptm_name fnlp/cpt-base
    --dataset ''
    --use_decoder 0
    --batch_size 16
    --update_every 1 运行以后，会报如下错误： RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument find_unused_parameters=True to torch.nn.parallel.DistributedDataParallel, and by making sure all forward function outputs participate in calculating loss.

    opened by suhejian 6
  • CPT多GPU卡finetuning训练报错

    CPT多GPU卡finetuning训练报错

    设置完模型参数后，使用python -m torch.distributed.launch --nproc_per_node 4 run_gen.py报错，local_rank需要作为参数进行传入，若parser.add_argument中增加--local_rank传入参数，整体多GPU训练报错。请问该如何对CPT进行多GPU卡的finetuning。恳求大佬给一份官方的使用说明！！感谢！

    opened by aidejieceng 4
  • CPTForConditionalGeneration使用多GPU报错

    CPTForConditionalGeneration使用多GPU报错

    如题，在做生成任务时，使用多GPU调用该接口会出现如下报错：

    RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. Since `find_unused_parameters=True` is enabled, this likely means that not all `forward` outputs participate in computing loss. You can fix this by making sure all `forward` function outputs participate in calculating loss.
If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
Parameter indices which did not receive grad for rank 2: 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388

    需要注意的是，一模一样的代码，我将CPTForConditionalGeneration接口换成BartForConditionalGeneration使用相应模型不会出现任何问题，请检查一下

    opened by Biaocsu 4
  • 可否提供run_gen.py的bart版本？

    可否提供run_gen.py的bart版本？

    路径下CPT/blob/master/finetune/generation/run_gen.py是CPT的版本 我自己按照这个改了一个bart版本，但是显示有很多层not used或者not initialized。 Some weights of the model checkpoint at model/bart-base-chinese were not used when initializing BartForConditionalGeneration Some weights of BartForConditionalGeneration were not initialized 不知道这些警告是否有影响，或者能否提供一个run_gen.py的bart版本？

    详细信息如下所示：

    loading weights file model/bart-base-chinese/pytorch_model.bin
Some weights of the model checkpoint at model/bart-base-chinese were not used when initializing BartForConditionalGeneration: ['encoder.layers.4.fc1.bias',
 'encoder.layers.0.self_attn.k_proj.bias',
 'encoder.layers.3.fc1.bias',
 'encoder.layers.4.fc1.weight',
 'encoder.layers.1.final_layer_norm.bias',
 'encoder.layers.0.fc2.weight',
 'encoder.layers.0.self_attn.out_proj.bias',
 'encoder.layers.1.self_attn.out_proj.weight',
 'encoder.layers.3.self_attn.k_proj.bias',
 'encoder.layernorm_embedding.weight',
 'encoder.layers.1.fc2.weight',
 'encoder.layers.5.self_attn.q_proj.weight',
 'encoder.layers.5.self_attn.q_proj.bias',
 'encoder.layers.0.final_layer_norm.weight',
 'encoder.layers.1.self_attn.v_proj.weight',
 'encoder.layers.4.self_attn.out_proj.weight',
 'encoder.layers.5.self_attn_layer_norm.bias',
 'encoder.layers.0.self_attn_layer_norm.bias',
 'encoder.layers.3.self_attn.k_proj.weight',
 'encoder.embed_tokens.weight',
 'encoder.layers.1.self_attn.v_proj.bias',
 'encoder.layers.5.final_layer_norm.bias',
 'encoder.layers.1.fc1.weight',
 'encoder.layers.5.self_attn_layer_norm.weight',
 'encoder.layers.2.fc1.weight',
 'encoder.layers.0.final_layer_norm.bias',
 'encoder.layers.1.fc2.bias',
 'encoder.layers.3.self_attn.v_proj.weight',
 'encoder.layers.3.final_layer_norm.bias',
 'encoder.layers.2.fc1.bias',
 'encoder.layers.3.self_attn.q_proj.weight',
 'encoder.layers.1.final_layer_norm.weight',
 'encoder.layers.4.fc2.bias',
 'encoder.layers.4.self_attn.out_proj.bias',
 'encoder.layers.2.self_attn.q_proj.weight',
 'encoder.layers.2.final_layer_norm.weight',
 'encoder.embed_positions.weight',
 'encoder.layers.3.self_attn.out_proj.bias',
 'encoder.layers.3.fc1.weight',
 'encoder.layers.1.fc1.bias',
 'encoder.layers.0.self_attn.k_proj.weight',
 'encoder.layers.1.self_attn.k_proj.bias',
 'encoder.layers.0.fc2.bias',
 'encoder.layers.1.self_attn.k_proj.weight',
 'encoder.layers.5.self_attn.v_proj.bias',
 'encoder.layers.1.self_attn.q_proj.weight',
 'encoder.layers.2.final_layer_norm.bias',
 'encoder.layers.4.self_attn_layer_norm.weight',
 'encoder.layers.4.self_attn.v_proj.bias',
 'encoder.layers.2.self_attn_layer_norm.weight',
 'encoder.layers.0.fc1.weight',
 'encoder.layers.4.self_attn.k_proj.bias',
 'encoder.layers.0.self_attn.q_proj.bias',
 'encoder.layers.4.final_layer_norm.bias',
 'encoder.layers.0.self_attn.v_proj.weight',
 'encoder.layers.3.final_layer_norm.weight',
 'encoder.layers.5.self_attn.out_proj.weight',
 'encoder.layers.4.self_attn.q_proj.weight',
 'encoder.layers.0.self_attn_layer_norm.weight',
 'encoder.layers.5.self_attn.v_proj.weight',
 'encoder.layers.2.self_attn.v_proj.weight',
 'encoder.layers.1.self_attn.out_proj.bias',
 'encoder.layers.2.self_attn.k_proj.bias',
 'encoder.layers.2.self_attn.out_proj.weight',
 'encoder.layers.3.self_attn.v_proj.bias',
 'encoder.layers.2.self_attn.q_proj.bias',
 'encoder.layers.2.self_attn.out_proj.bias',
 'encoder.layers.3.fc2.bias',
 'encoder.layers.5.fc1.weight',
 'encoder.layernorm_embedding.bias',
 'encoder.layers.0.fc1.bias',
 'encoder.layers.3.self_attn_layer_norm.bias',
 'encoder.layers.5.self_attn.k_proj.weight',
 'encoder.layers.5.fc1.bias',
 'encoder.layers.3.fc2.weight',
 'encoder.layers.4.fc2.weight',
 'encoder.layers.0.self_attn.v_proj.bias',
 'encoder.layers.0.self_attn.q_proj.weight',
 'encoder.layers.1.self_attn.q_proj.bias',
 'encoder.layers.3.self_attn_layer_norm.weight',
 'encoder.layers.2.self_attn.k_proj.weight',
 'encoder.layers.2.self_attn.v_proj.bias',
 'encoder.layers.5.final_layer_norm.weight',
 'encoder.layers.5.self_attn.out_proj.bias',
 'encoder.layers.0.self_attn.out_proj.weight',
 'encoder.layers.5.fc2.weight',
 'encoder.layers.5.fc2.bias',
 'encoder.layers.1.self_attn_layer_norm.bias',
 'encoder.layers.4.self_attn.k_proj.weight',
 'encoder.layers.5.self_attn.k_proj.bias',
 'encoder.layers.3.self_attn.q_proj.bias',
 'encoder.layers.4.self_attn.q_proj.bias',
 'encoder.layers.1.self_attn_layer_norm.weight',
 'encoder.layers.2.self_attn_layer_norm.bias',
 'encoder.layers.4.final_layer_norm.weight',
 'encoder.layers.4.self_attn.v_proj.weight',
 'encoder.layers.2.fc2.weight',
 'encoder.layers.2.fc2.bias',
 'encoder.layers.4.self_attn_layer_norm.bias',
 'encoder.layers.3.self_attn.out_proj.weight']
- This IS expected if you are initializing BartForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BartForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BartForConditionalGeneration were not initialized from the model checkpoint at model/bart-base-chinese and are newly initialized: 
['encoder.encoder.layer.1.output.dense.bias',
 'encoder.encoder.layer.3.attention.self.key.bias',
 'encoder.encoder.layer.3.attention.output.LayerNorm.weight',
 'encoder.encoder.layer.4.attention.self.value.bias',
 'encoder.encoder.layer.2.attention.output.dense.bias',
 'encoder.encoder.layer.4.output.LayerNorm.bias',
 'encoder.encoder.layer.4.output.LayerNorm.weight',
 'encoder.encoder.layer.4.attention.output.LayerNorm.weight',
 'encoder.encoder.layer.0.intermediate.dense.bias',
 'encoder.encoder.layer.5.attention.output.LayerNorm.weight',
 'encoder.encoder.layer.0.output.LayerNorm.bias',
 'encoder.encoder.layer.5.attention.output.LayerNorm.bias',
 'encoder.encoder.layer.2.attention.output.LayerNorm.weight',
 'encoder.encoder.layer.2.attention.self.key.weight',
 'encoder.embeddings.LayerNorm.weight',
 'encoder.encoder.layer.0.attention.output.LayerNorm.weight',
 'encoder.encoder.layer.1.attention.self.key.bias',
 'encoder.encoder.layer.3.intermediate.dense.weight',
 'encoder.encoder.layer.5.intermediate.dense.weight',
 'encoder.encoder.layer.0.output.dense.weight',
 'encoder.encoder.layer.5.output.LayerNorm.bias',
 'encoder.encoder.layer.1.output.dense.weight',
 'encoder.encoder.layer.5.attention.self.query.weight',
 'encoder.encoder.layer.1.output.LayerNorm.weight',
 'encoder.encoder.layer.4.attention.self.key.bias',
 'encoder.encoder.layer.3.output.LayerNorm.bias',
 'encoder.encoder.layer.5.output.dense.bias',
 'encoder.encoder.layer.4.attention.self.key.weight',
 'encoder.encoder.layer.0.attention.self.key.bias',
 'encoder.encoder.layer.0.attention.self.query.weight',
 'encoder.encoder.layer.0.intermediate.dense.weight',
 'encoder.encoder.layer.3.output.LayerNorm.weight',
 'encoder.encoder.layer.3.attention.output.dense.bias',
 'encoder.encoder.layer.5.output.dense.weight',
 'encoder.embeddings.LayerNorm.bias',
 'encoder.encoder.layer.1.attention.self.value.weight',
 'encoder.encoder.layer.2.output.dense.weight',
 'encoder.encoder.layer.4.intermediate.dense.weight',
 'encoder.encoder.layer.2.attention.self.value.weight',
 'encoder.encoder.layer.0.attention.self.value.weight',
 'encoder.encoder.layer.0.attention.output.dense.bias',
 'encoder.encoder.layer.2.attention.output.LayerNorm.bias',
 'encoder.encoder.layer.3.output.dense.bias',
 'encoder.encoder.layer.5.output.LayerNorm.weight',
 'encoder.encoder.layer.5.attention.output.dense.bias',
 'encoder.encoder.layer.4.attention.self.value.weight',
 'encoder.encoder.layer.3.attention.self.query.bias',
 'encoder.encoder.layer.3.attention.self.value.weight',
 'encoder.encoder.layer.3.attention.self.key.weight',
 'encoder.encoder.layer.0.output.dense.bias',
 'encoder.encoder.layer.1.intermediate.dense.bias',
 'encoder.encoder.layer.0.attention.self.query.bias',
 'encoder.encoder.layer.1.intermediate.dense.weight',
 'encoder.encoder.layer.0.attention.output.dense.weight',
 'encoder.encoder.layer.5.attention.self.value.bias',
 'encoder.embeddings.token_type_embeddings.weight',
 'encoder.encoder.layer.1.attention.output.dense.weight',
 'encoder.encoder.layer.2.attention.self.query.bias',
 'encoder.encoder.layer.2.attention.self.query.weight',
 'encoder.encoder.layer.2.attention.output.dense.weight',
 'encoder.encoder.layer.5.attention.self.query.bias',
 'encoder.embeddings.position_ids',
 'encoder.embeddings.position_embeddings.weight',
 'encoder.encoder.layer.3.attention.self.query.weight',
 'encoder.embeddings.word_embeddings.weight',
 'encoder.encoder.layer.4.output.dense.bias',
 'encoder.encoder.layer.1.attention.output.LayerNorm.weight',
 'encoder.encoder.layer.4.attention.self.query.bias',
 'encoder.encoder.layer.3.attention.self.value.bias',
 'encoder.encoder.layer.5.intermediate.dense.bias',
 'encoder.encoder.layer.1.output.LayerNorm.bias',
 'encoder.encoder.layer.3.attention.output.dense.weight',
 'encoder.encoder.layer.3.attention.output.LayerNorm.bias',
 'encoder.encoder.layer.2.output.LayerNorm.weight',
 'encoder.encoder.layer.4.attention.output.dense.weight',
 'encoder.encoder.layer.4.intermediate.dense.bias',
 'encoder.encoder.layer.2.attention.self.value.bias',
 'encoder.encoder.layer.0.attention.self.key.weight',
 'encoder.encoder.layer.1.attention.self.query.weight',
 'encoder.encoder.layer.2.intermediate.dense.bias',
 'encoder.encoder.layer.2.intermediate.dense.weight',
 'encoder.encoder.layer.5.attention.self.key.bias',
 'encoder.encoder.layer.2.attention.self.key.bias',
 'encoder.encoder.layer.2.output.LayerNorm.bias',
 'encoder.encoder.layer.5.attention.self.key.weight',
 'encoder.encoder.layer.0.attention.output.LayerNorm.bias',
 'encoder.encoder.layer.5.attention.self.value.weight',
 'encoder.encoder.layer.4.attention.output.dense.bias',
 'encoder.encoder.layer.1.attention.output.LayerNorm.bias',
 'encoder.encoder.layer.1.attention.output.dense.bias',
 'encoder.encoder.layer.5.attention.output.dense.weight',
 'encoder.encoder.layer.4.output.dense.weight',
 'encoder.encoder.layer.0.attention.self.value.bias',
 'encoder.encoder.layer.1.attention.self.value.bias',
 'encoder.encoder.layer.0.output.LayerNorm.weight',
 'encoder.encoder.layer.1.attention.self.key.weight',
 'encoder.encoder.layer.3.intermediate.dense.bias',
 'encoder.encoder.layer.1.attention.self.query.bias',
 'encoder.encoder.layer.4.attention.self.query.weight',
 'encoder.encoder.layer.3.output.dense.weight',
 'encoder.encoder.layer.2.output.dense.bias',
 'encoder.encoder.layer.4.attention.output.LayerNorm.bias']
    opened by 6666ev 3
  • 有关fnlp/bart-base-chinese模型加载问题

    有关fnlp/bart-base-chinese模型加载问题

    你好： 我参考A Unified Generative Framework for Aspect-Based Sentiment这篇文章，想用这个模型作中文的ABSA，于是我将原文的facebook/bart-base替换成fnlp/bart-base-chinese，但是我这里有以下几个问题： 1：transformers在4.4.1版本加载模型时会报错：RuntimeError: Error(s) in loading state_dict for BartModel: size mismatch for encoder.embed_positions.weight: copying a param with shape torch.Size([514, 768]) from checkpoint, the shape in current model is torch.Size([512, 768]). size mismatch for encoder.embed_positions.weight: copying a param with shape torch.Size([514, 768]) from checkpoint, the shape in current model is torch.Size([512, 768]). 这主要是在这里：model = BartSeq2SeqModel.build_model(bart_name, tokenizer, label_ids=label_ids, decoder_type=decoder_type,copy_gate=False, use_encoder_mlp=use_encoder_mlp, use_recur_pos=False) 2：facebook提供的batr-base中有一些文件是merges.txt和json形式的vocab，这与您在huggingface上提供的不一致。我将您在 huggingface上提供的有关bart-base-chinese提供的文件用tokenizer.from_pretrained("bart-base-chinese")使用时，pytorch报错： OSError: Can't load tokenizer for 'bart-base-chinese'. Make sure that: - 'bart-base-chinese' is a correct model identifier listed on 'https://huggingface.co/models' - or 'bart-base-chinese' is the correct path to a directory containing relevant tokenizer files 请问这个该怎么解决？

    opened by yedongyu1996 2
  • 使用自定义数据集在bart-base-chinese的继续pretrain

    使用自定义数据集在bart-base-chinese的继续pretrain

    我想要在自己的数据集上使用Huggingface已经开源的bart-base-chinese的继续pretrain流程，但是在training.py中load_checkpoint加载模型步骤遇到了一个问题。 load_checkpoint函数中，需要得到一个tracker file，如果不存在这个文件便会有警告“will not load any checkpoints and will start from random”，但是我希望从bart-base-chinese的基础上进行pretrain，请问这个tracker file应该如何设置？以及后面torch.load是应该直接加载pytorch_model.bin吗？但是它似乎不是代码里提及的model_optim_rng.pt。

    opened by Aureole-1210 2
  • 想咨询run_gen.py如何设置GPU运行？

    想咨询run_gen.py如何设置GPU运行？

    您好！我直接运行 whj_code1/projects/CPT/finetune/generation/run_gen.py 代码，发现是用CPU运行的。我看到日志中输出了 training_args.local_rank、training_args.device、training_args.n_gpu 参数，但是我发现代码中没有提供传参的位置，而且我也无法直接通过args传递这些参数。所以想咨询如何修改代码，使其能够用GPU来运行呢？

    opened by PolarisRisingWar 2
  • max_position_embeddings是1024吗

    max_position_embeddings是1024吗

    我看fnlp/cpt-base里面config.json的max_position_embeddings写的1024，但实际上1024会报错，512没问题。 发现代码里用了BertModel当encoder，但是没设置对应的max_position_embeddings 手动改成1024会导致预训练参数加载不进来。 所以我的理解是config.json写错了，实际只支持512。希望能提供一版max_position_embeddings=1024的模型，和bart对齐一下

    opened by awdrgyjilplij 2
  • why BertTokenizer is used instead of BartTokenizer?

    why BertTokenizer is used instead of BartTokenizer?

    Thank you for your nice work!

    when preprocessing data, I follow your code to use BertTokenizer to load the cpt-base tokenizer. The tokenizer is load successfully, but I get the following warning message:

    """ The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. The tokenizer class you load from this checkpoint is 'BartTokenizer'. The class this function is called from is 'BertTokenizer'. """

    Then I tried to use BartTokenizer to load it, but I failed.

    The question is whether I should ignore the warning and still use the BertTokenizer? Thank you.

    opened by Chen-Wang-CUHK 2
Releases(v2.0)
Owner
fastNLP
由复旦大学的自然语言处理（NLP）团队发起的国产自然语言处理开源项目
fastNLP
GitHub Repository
Code to reproduce experiments in the paper "Explainability Requires Interactivity".

Explainability Requires Interactivity This repository contains the code to train all custom models used in the paper Explainability Requires Interacti

Digital Health & Machine Learning 5 Apr 07, 2022
AI创造营 ：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

paddle-wechaty-Zodiac AI创造营 ：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人 12星座若穿越科幻剧，会拥有什么超能力呢？快来迎接你的专属超能力吧！ 现在很多年轻人都喜欢看科幻剧，像是复仇者系列，里面有很多英雄、超

105 Dec 22, 2022
A new GCN model for Point Cloud Analyse

Pytorch Implementation of PointNet and PointNet++ This repo is implementation for VA-GCN in pytorch. Classification (ModelNet10/40) Data Preparation D

12 Feb 02, 2022
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks [Paper] [Project Website] This repository holds the source code, pretra

Humam Alwassel 83 Dec 21, 2022
Source code for ZePHyR: Zero-shot Pose Hypothesis Rating @ ICRA 2021

ZePHyR: Zero-shot Pose Hypothesis Rating ZePHyR is a zero-shot 6D object pose estimation pipeline. The core is a learned scoring function that compare

R-Pad - Robots Perceiving and Doing 18 Aug 22, 2022
Unsupervised Image-to-Image Translation

UNIT: UNsupervised Image-to-image Translation Networks Imaginaire Repository We have a reimplementation of the UNIT method that is more performant. It

Ming-Yu Liu 劉洺堉 1.9k Dec 26, 2022
This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

Rotate-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes. Section I. Description The codes are

xinzelee 90 Dec 13, 2022
Neural Surface Maps

Neural Surface Maps Official implementation of Neural Surface Maps - Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra [Paper] [Project Page]

Luca Morreale 49 Dec 13, 2022
[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore

[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6101 of Semester 1, AY2021-2022, starting from 08/2021. The instructors of

AccSrd 1 Sep 22, 2022
Reinfore learning tool box, contains trpo, a3c algorithm for continous action space

RL_toolbox all the algorithm is running on pycharm IDE, or the package loss error may exist. implemented algorithm: trpo a3c a3c:for continous action

yupei.wu 44 Oct 10, 2022
Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer This repository contains the PyTorch code for Evo-ViT. This work proposes a slow-fas

YifanXu 53 Dec 05, 2022
Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

advantage-weighted-regression Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning, by Peng et al. (

Omar D. Domingues 1 Dec 02, 2021
VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations

VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations 3D-aware Image Synthesis via Learning Structural and Textura

GenForce: May Generative Force Be with You 116 Dec 26, 2022
LineBoard - Python+React+MySQL-白板即時系統改善人群行為

LineBoard-白板即時系統改善人群行為 即時顯示實驗室的使用狀況，並遠端預約排隊，以此來改善人們的工作效率 程式架構 運作流程 使用者先至該實驗室網站預約

Bo-Jyun Huang 1 Feb 22, 2022
[CVPRW 2021] Code for Region-Adaptive Deformable Network for Image Quality Assessment

RADN [CVPRW 2021] Code for Region-Adaptive Deformable Network for Image Quality Assessment [Paper on arXiv] Overview Update [2021/5/7] add codes for W

IIGROUP 53 Dec 28, 2022
Face Transformer for Recognition

Face-Transformer This is the code of Face Transformer for Recognition (https://arxiv.org/abs/2103.14803v2). Recently there has been great interests of

Zhong Yaoyao 153 Nov 30, 2022
Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.

Lunar Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs. About Lunar can be modified to work

Zeyad Mansour 276 Jan 07, 2023
Music Generation using Neural Networks Streamlit App

Music_Gen_Streamlit "Music Generation using Neural Networks" Streamlit App TO DO: Make a run_app.sh Introduction [~5 min] (Sohaib) Team Member names/i

Muhammad Sohaib Arshid 6 Aug 09, 2022
A Python Package for Convex Regression and Frontier Estimation

pyStoNED pyStoNED is a Python package that provides functions for estimating multivariate convex regression, convex quantile regression, convex expect

Sheng Dai 17 Jan 08, 2023
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. Hyperactive: is very easy to lear

Simon Blanke 422 Jan 04, 2023