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

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_ranktraining_args.devicetraining_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
A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework".

Mugs: A Multi-Granular Self-Supervised Learning Framework This is a PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-

Sea AI Lab 62 Nov 08, 2022
Learning and Building Convolutional Neural Networks using PyTorch

Image Classification Using Deep Learning Learning and Building Convolutional Neural Networks using PyTorch. Models, selected are based on number of ci

Mayur 126 Dec 22, 2022
The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble

Wordle RL The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble I know there are more deterministic

Aditya Arora 3 Feb 22, 2022
This is the code related to "Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation" (ICCV 2021).

Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation This is the code relat

39 Sep 23, 2022
Pytorch implementation of face attention network

Face Attention Network Pytorch implementation of face attention network as described in Face Attention Network: An Effective Face Detector for the Occ

Hooks 312 Dec 09, 2022
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
Code for "ATISS: Autoregressive Transformers for Indoor Scene Synthesis", NeurIPS 2021

ATISS: Autoregressive Transformers for Indoor Scene Synthesis This repository contains the code that accompanies our paper ATISS: Autoregressive Trans

138 Dec 22, 2022
Learning to See by Looking at Noise

Learning to See by Looking at Noise This is the official implementation of Learning to See by Looking at Noise. In this work, we investigate a suite o

Manel Baradad Jurjo 82 Dec 24, 2022
Predicting the duration of arrival delays for commercial flights.

Flight Delay Prediction Our objective is to predict arrival delays of commercial flights. According to the US Department of Transportation, about 21%

Jordan Silke 1 Jan 11, 2022
Official code for the paper: Deep Graph Matching under Quadratic Constraint (CVPR 2021)

QC-DGM This is the official PyTorch implementation and models for our CVPR 2021 paper: Deep Graph Matching under Quadratic Constraint. It also contain

Quankai Gao 55 Nov 14, 2022
Out-of-Town Recommendation with Travel Intention Modeling (AAAI2021)

TrainOR_AAAI21 This is the official implementation of our AAAI'21 paper: Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, Hui Xiong

Jack Xin 13 Oct 19, 2022
A PyTorch Toolbox for Face Recognition

FaceX-Zoo FaceX-Zoo is a PyTorch toolbox for face recognition. It provides a training module with various supervisory heads and backbones towards stat

JDAI-CV 1.6k Jan 06, 2023
[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang

Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective [PDF] Wuyang Chen, Xinyu Gong, Zhangyang Wang In ICLR 2

VITA 156 Nov 28, 2022
Differentiable Factor Graph Optimization for Learning Smoothers @ IROS 2021

Differentiable Factor Graph Optimization for Learning Smoothers Overview Status Setup Datasets Training Evaluation Acknowledgements Overview Code rele

Brent Yi 60 Nov 14, 2022
Adaout is a practical and flexible regularization method with high generalization and interpretability

Adaout Adaout is a practical and flexible regularization method with high generalization and interpretability. Requirements python 3.6 (Anaconda versi

lambett 1 Feb 09, 2022
Python wrapper of LSODA (solving ODEs) which can be called from within numba functions.

numbalsoda numbalsoda is a python wrapper to the LSODA method in ODEPACK, which is for solving ordinary differential equation initial value problems.

Nick Wogan 52 Jan 09, 2023
i3DMM: Deep Implicit 3D Morphable Model of Human Heads

i3DMM: Deep Implicit 3D Morphable Model of Human Heads CVPR 2021 (Oral) Arxiv | Poject Page This project is the official implementation our work, i3DM

Tarun Yenamandra 60 Jan 03, 2023
Using deep learning model to detect breast cancer.

Breast-Cancer-Detection Breast cancer is the most frequent cancer among women, with around one in every 19 women at risk. The number of cases of breas

1 Feb 13, 2022
[2021][ICCV][FSNet] Full-Duplex Strategy for Video Object Segmentation

Full-Duplex Strategy for Video Object Segmentation (ICCV, 2021) Authors: Ge-Peng Ji, Keren Fu, Zhe Wu, Deng-Ping Fan*, Jianbing Shen, & Ling Shao This

Daniel-Ji 55 Dec 22, 2022