atcbosselut / comet-commonsense

Code for ACL 2019 Paper: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction" https://arxiv.org/abs/1906.05317

To run a generation experiment (either conceptnet or atomic), follow these instructions:

First Steps

First clone, the repo:

git clone https://github.com/atcbosselut/comet-commonsense.git

Then run the setup scripts to acquire the pretrained model files from OpenAI, as well as the ATOMIC and ConceptNet datasets

bash scripts/setup/get_atomic_data.sh
bash scripts/setup/get_conceptnet_data.sh
bash scripts/setup/get_model_files.sh

Then install dependencies (assuming you already have Python 3.6 and Pytorch >= 1.0:

conda install tensorflow
pip install ftfy==5.1
conda install -c conda-forge spacy
python -m spacy download en
pip install tensorboardX
pip install tqdm
pip install pandas
pip install ipython

Making the Data Loaders

Run the following scripts to pre-initialize a data loader for ATOMIC or ConceptNet:

python scripts/data/make_atomic_data_loader.py
python scripts/data/make_conceptnet_data_loader.py

For the ATOMIC KG, if you'd like to make a data loader for only a subset of the relation types, comment out any relations in lines 17-25.

For ConceptNet if you'd like to map the relations to natural language analogues, set opt.data.rel = "language" in line 26. If you want to initialize unpretrained relation tokens, set opt.data.rel = "relation"

Setting the ATOMIC configuration files

Open config/atomic/changes.json and set which categories you want to train, as well as any other details you find important. Check src/data/config.py for a description of different options. Variables you may want to change: batch_size, learning_rate, categories. See config/default.json and config/atomic/default.json for default settings of some of these variables.

Setting the ConceptNet configuration files

Open config/conceptnet/changes.json and set any changes to the degault configuration that you may want to vary in this experiment. Check src/data/config.py for a description of different options. Variables you may want to change: batch_size, learning_rate, etc. See config/default.json and config/conceptnet/default.json for default settings of some of these variables.

Running the ATOMIC experiment

Training

For whichever experiment # you set in ```config/atomic/changes.json``` (e.g., 0, 1, 2, etc.), run:

python src/main.py --experiment_type atomic --experiment_num #

Evaluation

Once you've trained a model, run the evaluation script:

python scripts/evaluate/evaluate_atomic_generation_model.py --split $DATASET_SPLIT --model_name /path/to/model/file

Generation

Once you've trained a model, run the generation script for the type of decoding you'd like to do:

python scripts/generate/generate_atomic_beam_search.py --beam 10 --split $DATASET_SPLIT --model_name /path/to/model/file
python scripts/generate/generate_atomic_greedy.py --split $DATASET_SPLIT --model_name /path/to/model/file
python scripts/generate/generate_atomic_topk.py --k 10 --split $DATASET_SPLIT --model_name /path/to/model/file

Running the ConceptNet experiment

Training

For whichever experiment # you set in config/conceptnet/changes.json (e.g., 0, 1, 2, etc.), run:

python src/main.py --experiment_type conceptnet --experiment_num #

Development and Test set tuples are automatically evaluated and generated with greedy decoding during training

Generation

If you want to generate with a larger beam size, run the generation script

python scripts/generate/generate_conceptnet_beam_search.py --beam 10 --split $DATASET_SPLIT --model_name /path/to/model/file

Classifying Generated Tuples

To run the classifier from Li et al., 2016 on your generated tuples to evaluate correctness, first download the pretrained model from:

wget https://ttic.uchicago.edu/~kgimpel/comsense_resources/ckbc-demo.tar.gz
tar -xvzf ckbc-demo.tar.gz

then run the following script on the the generations file, which should be in .pickle format:

bash scripts/classify/classify.sh /path/to/generations_file/without/pickle/extension

If you use this classification script, you'll also need Python 2.7 installed.

Playing Around in Interactive Mode

First, download the pretrained models from the following link:

https://drive.google.com/open?id=1FccEsYPUHnjzmX-Y5vjCBeyRt1pLo8FB

Then untar the file:

tar -xvzf pretrained_models.tar.gz

Then run the following script to interactively generate arbitrary ATOMIC event effects:

python scripts/interactive/atomic_single_example.py --model_file pretrained_models/atomic_pretrained_model.pickle

Or run the following script to interactively generate arbitrary ConceptNet tuples:

python scripts/interactive/conceptnet_single_example.py --model_file pretrained_models/conceptnet_pretrained_model.pickle

Bug Fixes

Beam Search

In BeamSampler in sampler.py, there was a bug that made the scoring function for each beam candidate slightly different from normalized loglikelihood. Only sequences decoded with beam search are affected by this. It's been fixed in the repository, and seems to have little discernible impact on the quality of the generated sequences. If you'd like to replicate the exact paper results, however, you'll need to use the buggy beam search from before, by setting paper_results = True in Line 251 of sampler.py

References

Please cite this repository using the following reference:

@inproceedings{Bosselut2019COMETCT,
  title={COMET: Commonsense Transformers for Automatic Knowledge Graph Construction},
  author={Antoine Bosselut and Hannah Rashkin and Maarten Sap and Chaitanya Malaviya and Asli Çelikyilmaz and Yejin Choi},
  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year={2019}
}

comet-commonsense's Issues

python2 or python3 ?

you said that you assumed I have python 3.6, but why do you use python2 style like this???
comet-commonsense/scripts/classify/demo_bilinear.py
line 21 print 'can not find corresponding vector total:', array[0].lower()

Potential problems with BeamSampler in src/evaluate/sampler.py

It seems that the beam sampler is doing sth weird when computing losses: (the following line) in src/evaluate/sampler.py

beam_lls, top_beam_idxs = (hyp_beam_lls / temp_counts).topk(self.opt.eval.bs)

in the loop, temp_counts is being divided again and again, making the losses for tokens in the begining less and less important.

Can I use the input with a longer length?

Dear authors,

I found you set the max_event and max_affect to 17 and 35 respectively. However, the length of my input event is much longer than 17, can I change it according to my need?

After scanning your code, I tend to change opt['data']["maxe1"] in main_atomic.py and self.max_event in atomic.py, can it work?

Why do not excludes "none" answers in ATOMIC datasets?

Hello, Sir.

I have something to ask you about this code.

I wonder a reason of why you do not exclude 'none' target answer.

Your code seems to include such data samples like "PersonX loses personX's sight xNeed none " as a training data or test data.

I think learning to generate 'none' dose not seems to meaningful.

And I found same thing in COMET-ATOMIC 2020.

Is there any specific reasons?

Thanks.

Can save model into h5 file or other language model file

Can I Save this atomic model into h5 file or language model file(like : added_tokens.json,config.json,pytorch_model.bin,vocab.json..)

get_conceptnet_data.sh gives access denied error

I'm following the setup instructions but get_conceptnet_data.sh is returning errors:

bash scripts/setup/get_conceptnet_data.sh
--2020-05-17 11:44:00-- https://ttic.uchicago.edu/~kgimpel/comsense_resources/train100k.txt.gz
Resolving ttic.uchicago.edu (ttic.uchicago.edu)... 128.135.8.186
Connecting to ttic.uchicago.edu (ttic.uchicago.edu)|128.135.8.186|:443... connected.
OpenSSL: error:1425F102:SSL routines:ssl_choose_client_version:unsupported protocol
Unable to establish SSL connection.
...

Visiting https://ttic.uchicago.edu/~kgimpel/comsense_resources/ also gives a 403 access forbidden error.

Testing COMET to new ConceptNet-like words

Gongratulations for your work! What part of the code should I use in order to give as input a text file with some new words(begin node) and receive relations with generated new words/concepts (end node)? Thank you.

Having troubles in interactive mode generation when using GPU

My command is python3 scripts/interactive/pretrain_atomic_txt.py --device 0 --model_file pretrained_models/atomic_pretrained_model.pickle

What I got:
in forward h = self.transformer(x, sequence_mask) File "tmp/lib64/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__ result = self.forward(*input, **kwargs) File "tmp/comet-commonsense/src/models/gpt.py", line 184, in forward e = self.embed(x) File "lib64/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__ result = self.forward(*input, **kwargs) File "tmp/lib64/python3.6/site-packages/torch/nn/modules/sparse.py", line 114, in forward self.norm_type, self.scale_grad_by_freq, self.sparse) File "tmp/lib64/python3.6/site-packages/torch/nn/functional.py", line 1724, in embedding return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) RuntimeError: Expected object of device type cuda but got device type cpu for argument #3 'index' in call to _th_index_select

Same loss value is logged for different loss types

Hi,

Seems like token level averaged NLL loss will be logged for all loss types:

comet-commonsense/src/train/train.py

Line 118 in 070aad1

loss.item() / self.opt.train.dynamic.bs,

If you can confirm that this is indeed an issue, I can submit a pull request.

Thanks

Changing pad issue

Access issue

Paper mistake？

I guess 'relation' should be 'tuple'.

MASK tokens

Hello,

I have a question about MASK tokens in the input examples (Figure 3, paper). Which role do they play in training and predictions. Unfortunately, I could not find the explanation in the paper. What is the role of mask tokens between subject tokens and relation tokens (ConceptNet) when only object tokens need to be predicted?
Thank you very much in advance.

dear sir, i want to get pre-train model

i use this command with bash scripts/setup/get_model_files.sh, but the project seems to be stalled when gitting clone 36% of this files

RuntimeError: CUDA out of memory.

Hi, when I execute the training got:

(PyTorch) zys@zys:~/Documents/gits/comet-commonsense$ python src/main.py --experiment_type atomic --experiment_num 0
config/atomic/config_0.json
{'gpu_mode': 'T', 'gpu_index': 0, 'gpu_indices': [0, 1], 'multigpu': 'F', 'topk_size': 10, 'beam_size': 1, 'gen_seqlength': 40, 'eval_sampler': 'greedy', 'num_sequences': 1, 'generate_sequences': 1000, 'evaluate_sequences': 10000, 'random_seed': 123, 'optimizer': 'adam', 'batch_size': 64, 'learning_rate': 6.25e-05, 'clip': 1, 'loss': 'nll', 'weight_decay': 0, 'adam': {'b2': 0.999, 'b1': 0.9, 'e': 1e-08}, 'model': 'transformer', 'pretrain': 'gpt', 'hidden_dim': 768, 'num_layers': 12, 'num_heads': 12, 'embedding_dropout': 0.1, 'attention_dropout': 0.1, 'residual_dropout': 0.1, 'output_dropout': 0.1, 'activation': 'gelu', 'init': 'pt', 'trainer': 'iteration', 'iterations': 50000, 'cycle': 500, 'save_strategy': 'best', 'epochs': 20, 'toy': 'F', 'do_gen': 'F', 'save': 'T', 'test_save': 'F', 'dataset': 'atomic', 'categories': ['oEffect'], 'eval_categories': ['oEffect'], 'exp': 'generation', 'labels': 'individual', 'encoder_path': 'model/encoder_bpe_40000.json', 'bpe_path': 'model/vocab_40000.bpe', 'learning_rate_schedule': 'warmup_linear', 'learning_rate_warmup': 0.002, 'l2': 0.01, 'vector_l2': 'T'}
{'b2': 0.999, 'b1': 0.9, 'e': 1e-08}
DD{'net': DD{'model': 'transformer', 'nL': 12, 'nH': 12, 'hSize': 768, 'edpt': 0.1, 'adpt': 0.1, 'rdpt': 0.1, 'odpt': 0.1, 'pt': 'gpt', 'afn': 'gelu', 'init': 'pt'}, 'mle': 0, 'dataset': 'atomic', 'train': DD{'static': DD{'exp': 'generation', 'seed': 123, 'l2': 0.01, 'vl2': True, 'lrsched': 'warmup_linear', 'lrwarm': 0.002, 'clip': 1, 'loss': 'nll', 'b2': 0.999, 'b1': 0.9, 'e': 1e-08}, 'dynamic': DD{'lr': 6.25e-05, 'bs': 64, 'optim': 'adam'}}, 'model': 'transformer', 'exp': 'generation', 'data': DD{'categories': ['oEffect'], 'maxe1': 17, 'maxe2': 35, 'maxr': 1}, 'eval': DD{'bs': 1, 'smax': 40, 'sample': 'greedy', 'numseq': 1, 'gs': 1000, 'es': 10000, 'categories': ['oEffect']}, 'trainer': 'iteration', 'cycle': 500, 'iters': 50000}
Loading Data
Loading data from: data/atomic/processed/generation/categories_oEffect-maxe1_17-maxe2_35-maxr_1.pickle
61795
Done.
dict_keys(['data', 'sequences', 'masks', 'offsets', 'categories', 'opt', 'vocab_encoder', 'vocab_decoder', 'special_chars', 'max_event', 'max_effect', 'batch_size'])
Building Model
50
LOADING PRETRAINED TRANSFORMER
Loading weights...
Done.
Files will be logged at: results/losses/atomic-generation/iteration-500-50000/transformer/categories_oEffect-maxe1_17-maxe2_33-maxr_1/model_transformer-nL_12-nH_12-hSize_768-edpt_0.1-adpt_0.1-rdpt_0.1-odpt_0.1-pt_gpt-afn_gelu-init_pt-vSize_40532/exp_generation-seed_123-l2_0.01-vl2_T-lrsched_warmup_linear-lrwarm_0.002-clip_1-loss_nll-b2_0.999-b1_0.9-e_1e-08/bs_1-smax_40-sample_greedy-numseq_1-gs_1000-es_10000-categories_oEffect/6.25e-05_adam_64_0
Pushing to GPU: 0
Done.
Training
Logging Tensorboard Files at: logs/atomic-generation/iteration-500-50000/transformer/categories_oEffect-maxe1_17-maxe2_33-maxr_1/model_transformer-nL_12-nH_12-hSize_768-edpt_0.1-adpt_0.1-rdpt_0.1-odpt_0.1-pt_gpt-afn_gelu-init_pt-vSize_40532/exp_generation-seed_123-l2_0.01-vl2_T-lrsched_warmup_linear-lrwarm_0.002-clip_1-loss_nll-b2_0.999-b1_0.9-e_1e-08/bs_1-smax_40-sample_greedy-numseq_1-gs_1000-es_10000-categories_oEffect/6.25e-05_adam_64
0%| | 0/50000 [00:00<?, ?it/s]{'total_micro': [0], 'total_macro': [0], 'oEffect_micro': [0], 'oEffect_macro': [0]}
0%| | 1/50000 [00:00<10:46:40, 1.29it/s]Traceback (most recent call last):
File "src/main.py", line 16, in
main(args.experiment_num)
File "/home/zys/Documents/gits/comet-commonsense/src/main_atomic.py", line 125, in main
trainer.run()
File "/home/zys/Documents/gits/comet-commonsense/src/train/train.py", line 194, in run
self.cycle(bar, cycle_num)
File "/home/zys/Documents/gits/comet-commonsense/src/train/train.py", line 212, in cycle
loss, nums, reset = self.do_forward_pass(nums)
File "/home/zys/Documents/gits/comet-commonsense/src/train/train.py", line 159, in do_forward_pass
self.batch_variables)
File "/home/zys/Documents/gits/comet-commonsense/src/train/atomic_train.py", line 34, in batch
outputs = batch.batch_atomic_generate(opt, *args)
File "/home/zys/Documents/gits/comet-commonsense/src/train/batch.py", line 37, in batch_atomic_generate
attention_mask[:, :-1], loss_reduction="none")
File "/home/zys/Documents/gits/comet-commonsense/src/train/batch.py", line 108, in mle_steps
attention_mask, i)
File "/home/zys/Documents/gits/comet-commonsense/src/train/batch.py", line 125, in decode
return model(input_, sequence_mask=attention_mask)
File "/home/zys/anaconda3/envs/PyTorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "/home/zys/Documents/gits/comet-commonsense/src/models/gpt.py", line 212, in forward
lm_logits = lm_logits + self.pos_emb_mask
RuntimeError: CUDA out of memory. Tried to allocate 486.00 MiB (GPU 0; 7.76 GiB total capacity; 6.32 GiB already allocated; 48.56 MiB free; 437.98 MiB cached)
0%| | 1/50000 [00:00<13:40:12, 1.02it/s]

Could you tell me your minimum configuration of GPU? I use a NVIDIA2060 got these errors.

How to generate xIntent output file for a given input file in atomic_single_example.py

Hello!

Thank you for sharing your work! I want to generate xIntent for some sequences. Is there a way that I can give an input file and have the output be stored in another file?

Thanks for your time!

Frank

Feeding a bigger size of the input event than 18.

Just want to make sure. Are we meant to change the original setting values 17, 35 for ATOMIC to bigger values in order to feed with a longer sentence than 18 words?

Or can we feed with a long sentence?

Are there some research papers about text-to-set generation?

I know this question is a little out of topic. But it is helpful to me. Thank you.

Text-to-(word)set generation or sequence-to-(token)set generation.

For example, input a text and then output the tags for this text:

'Peter is studying English' --> {'good behavior','person','doing something'}

Thank you!

How can I use multigpu?

I want to know how to use multi-gpu and whether I need to implement multi-gpu version by myself?

Can this model give a knowledge graph according to the input sentence?

Can this model give a knowledge graph according to the input sentence?
Such as "One absolutely cardinal reason is the fact that universities offer an opportunity to intensify the knowledge in a particular field of interest."
Maybe this is a long sentence, I want to ask if our model has such a function?

Task:Classifying Generated Tupes

Hi, I want to implement the task ‘Classifying Generated Tupes’, I have the following questions:

1. Can the experiment be run under the windows system?

2. What version of torch is used for this task? The pytorch official website does not provide a version of the windows system corresponding to python2.7.

3. The interface when running the command ‘bash scripts/classify/classify.sh’,has no result.

how to generate in our own test file in ConceptNet?

I have completed training in ConceptNet. However, if I would like to generate in our own test file, how could I do? I have changed our test file into 'test.txt', but the length of generation is fixed in 1200 as same as the original 'test.txt' 's positive length, how can I fix the length the same as our own test? Thanku.

tensorboardX logdir name change

I believe the following change is needed to make comet-commonsense compatible with the latest tensorboardX:

src/train/train.py
83 print("Logging Tensorboard Files at: {}".format(self.logger.log_dir))
83 print("Logging Tensorboard Files at: {}".format(self.logger.logdir))

Atomic Evaluation

For atomic evaluation, the evaluation doesn't have bleu-2 score. Will you update the script to calculate the generation by bleu-2 score?

FileNotFoundError: [Errno 2] No such file or directory: 'comet/data/atomic/processed/generation/categories_oEffect#oReact#oWant#xAttr#xEffect#xIntent#xNeed#xReact#xWant-maxe1_17-maxe2_35-maxr_1.pickle'

FileNotFoundError: [Errno 2] No such file or directory: 'comet/data/atomic/processed/generation/categories_oEffect#oReact#oWant#xAttr#xEffect#xIntent#xNeed#xReact#xWant-maxe1_17-maxe2_35-maxr_1.pickle'

Environment install problems

In the README.md you need to change the tensorflow and spacy install methods. pip tensorflow downloads a different version than what is needed and works better if you just use conda to install it. Also, the spacy 3.0 that is default install now, doesn't work with your dataloader. Proposed changes:

pip install tensorflow --> conda install tensorflow
conda install -c conda-forge spacy --> conda install -c conda-forge spacy=2.3.5

A question regarding the model selection

Dear authors,

Thanks for the great work. I just have one small question about your paper. How is the model selected, especially for the conceptnet task? It is selected based on its performance on the dev set or you simply selected the final model after training?

Thank you so much for your help in advance.

Could you please recover the full sentence of this picture? Thank you!

I am confused about the alignment.

Different inference resullt

Hi,
I've tested a number of cases either using code&&parameter from this repo or demo from https://mosaickg.apps.allenai.org/comet_atomic. And I'm kind of confused as there have obvious differences in the results. So what's the exact difference between them?
Thanks

Results are different with python versions

I am trying to construct a codebase for my ACL paper where I used comet
I tested my code previously on 3.7 but now due to an additional feature i downgraded to 3.6 with that the COMET results are differing

For example for 👍
cereal crisp terrible dinner

3.6
['cereal', 'breakfast', 'drink milk', 'eat it', 'coffee']

3.7
['stomach ache', 'eat it', 'bad breath', 'vomit', 'you will feel bad']

Under such situations reproducibility is an issue
i set paperresults = True for both cases

any way this can be made to work

where does the model pickle file gets saved?

URL of ATOMIC dataset is not available

scripts/setup/get_atomic_data.sh tries to download atomic_data.tgz from washington.edu but the URL is not available.
Probably the URL could be changed to https://storage.googleapis.com/ai2-mosaic/public/atomic/v1.0/atomic_data.tgz.

Difficulty reproducing ConceptNet scores

I'm having some difficulty reproducing the conceptnet accuracy scores, could you please point me in the right direction? These are the steps I took:

1. Download pretrained COMET model from https://drive.google.com/open?id=1FccEsYPUHnjzmX-Y5vjCBeyRt1pLo8FB
2. Run generations with greedy decoding on conceptnet test set
3. Use Bilinear AVG model by Li et al. (2016) to score generated tuples and threshold by 0.5 to get accuracy

However, because I achieved an accuracy of 80.04%, which is much lower than the 95.25% reported in your Table, I'm sure I must be doing something wrong! The (short) code I used to get the scores is here: https://colab.research.google.com/drive/10oaX-_1qS75xrgbm67MDkRy_hOHEJA0s

Is it possible to limit the vocabulary set of generation?

Hi,

I wonder if it is possible to use the trained comet model, however limit the vocabulary set of generation? In other words have a customized subset of the current vocabulary set and force the model to only generate from that?
I need this specifically for one of the nine available dimensions.

Thanks in advance,

Question about Making the Data loaders

Dear author :
I follow the instruction in the Readme file,when i try to make the data loaders about conceptNet,i came across the question:
~/comet-commonsense$ python scripts/data/make_conceptnet_data_loader.py
100%███████████████████████████████████████████| 100000/100000 [04:06<00:00, 405.46it/s]
100%███████████████████████████████████████████████████| 2400/2400 [00:05<00:00, 408.77it/s]
100%|████████████████████████████████████| 2400/2400 [00:05<00:00, 426.74it/s]
28
16
16
dev
Traceback (most recent call last):
File "scripts/data/make_conceptnet_data_loader.py", line 66, in
data_loader.make_tensors(text_encoder, special, test=False)
File "/home/caihanqing/comet-commonsense/src/data/conceptnet.py", line 196, in make_tensors
self.data[split]['total']) if not j[3]]))
RuntimeError: index out of range: Tried to access index 2306 out of table with 2305 rows. at /opt/conda/conda-bld/pytorch_1570910687230/work/aten/src/TH/generic/THTensorEvenMoreMath.cpp:418
Can you give me some useful advice to my further training ? @madaan @atcbosselut

question about the runtime error

Here is the detail of the question :
File "comet-commonsense/src/interactive/functions.py", line 124, in get_atomic_sequence
input_event, category, data_loader, text_encoder)
File "comet-commonsense/src/interactive/functions.py", line 158, in set_atomic_inputs
XMB[:, :len(prefix)] = torch.LongTensor(prefix)
RuntimeError: The expanded size of the tensor (18) must match the existing size (93) at non-singleton dimension 1. Tar get sizes: [1, 18]. Tensor sizes: [93]
do you know how to deal with it ? @madaan

In the paper, Figure 2 and Figure 3 seems conflict.

In Figure 2(c), the model input the 0:n-1 words to predict the n word. Or the model input s and r predict o.

But in Figure 3, the model input all the tuple s r o.

What does Top-N random sampling mean?

I am confused about how to sample. I know what beam search is.
Thank you.

Is the model a transformer encoder-decoder or just decoder?

Hi,

Thank you for the very nice and interesting work.
I have a question regarding the model. In the paper it s mentioned that you used the same architecture as GPT, which is a transformer decoder. However, you also talked about input encoder and how you configure the input to encoder.
I was a bit confused, whether you have both encoder and decoder? or it is a language model and then it would be just decoder. And if it is a decoder, then how do you encode your [X_s, X_r] ?
Could you please clarify this. Many thanks

Can your code support multi-gpu training?

Dear authors,

Due to the GPU memory limitation of my devices, I tend to train the model with multi-GPU. After scanning your code, I modified the config/atomic/config_2.json （my experiment_num is 2）and config/default.json to set multi-GPU, as shown below:

• config/atomic/config_2.json:
  

• config/default.json :

Can I run this project on the CPU ？

Can I run this project on the CPU? I don't have enough GPU conditions