structure

mm-cot/CODE_OF_CONDUCT.md

@@ -1,4 +0,0 @@
-## Code of Conduct
-This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
-For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
-opensource-codeofconduct@amazon.com with any additional questions or comments.

mm-cot/CONTRIBUTING.md

@@ -1,59 +0,0 @@
-# Contributing Guidelines
-
-Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
-documentation, we greatly value feedback and contributions from our community.
-
-Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
-information to effectively respond to your bug report or contribution.
-
-
-## Reporting Bugs/Feature Requests
-
-We welcome you to use the GitHub issue tracker to report bugs or suggest features.
-
-When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already
-reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
-
-* A reproducible test case or series of steps
-* The version of our code being used
-* Any modifications you've made relevant to the bug
-* Anything unusual about your environment or deployment
-
-
-## Contributing via Pull Requests
-Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
-
-1. You are working against the latest source on the *main* branch.
-2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.
-3. You open an issue to discuss any significant work - we would hate for your time to be wasted.
-
-To send us a pull request, please:
-
-1. Fork the repository.
-2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
-3. Ensure local tests pass.
-4. Commit to your fork using clear commit messages.
-5. Send us a pull request, answering any default questions in the pull request interface.
-6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
-
-GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and
-[creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
-
-
-## Finding contributions to work on
-Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.
-
-
-## Code of Conduct
-This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
-For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
-opensource-codeofconduct@amazon.com with any additional questions or comments.
-
-
-## Security issue notifications
-If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
-
-
-## Licensing
-
-See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.

mm-cot/LICENSE

@@ -1,175 +0,0 @@
-
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-                        http://www.apache.org/licenses/
-
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mm-cot/NOTICE

@@ -1 +0,0 @@
-Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

mm-cot/README.md

@@ -1,93 +0,0 @@
-# Multimodal Chain-of-Thought Reasoning in Language Models
-
-<h5 align="center"><i>"Imagine learning a textbook without figures or tables."</i></h5>
-
-Multimodal-CoT incorporates vision features in a decoupled training framework. The framework consists of two training stages: (i) rationale generation and (ii) answer inference. Both stages share the same model architecture but differ in the input and output.
-
-![](vision_features/mm-cot.png)
-
-
-## Requirements
-
-Install all required python dependencies:
-
-```
-pip install -r requirements.txt
-```
-
-## Datasets
-
-Download the dataset from the following repository:
-
-```
-https://github.com/lupantech/ScienceQA/tree/main/data
-```
-
-Download the extracted vision features from [vision_features](https://drive.google.com/file/d/13B0hc_F_45-UlqPLKSgRz-ALtFQ8kIJr/view?usp=share_link) and unzip the files under `vision_features`
-
-## Instructions
-
-### Training 
-
-```
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_eval.json \
-    --test_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_test.json
-```
-
-### Inference 
-
-Our trained models are available at [models](https://drive.google.com/file/d/1FtTYOJPHnWnFfCxNC6M3gar4RAX5E21b/view?usp=share_link). To use our trained models, please put the them under the ```models``` folder.
-
-```
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE \
-    --evaluate_dir models/MM-CoT-UnifiedQA-base-Rationale
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le models/rationale/predictions_ans_eval.json \
-    --test_le models/rationale/predictions_ans_test.json \
-    --evaluate_dir models/MM-CoT-UnifiedQA-base-Answer
-```
-
-## Citing MM-CoT
-
-```
-@article{zhang2023multicot,
-  title={Multimodal Chain-of-Thought Reasoning in Language Models},
-  author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Zhao, Hai and Karypis, George and Smola, Alex},
-  journal={arXiv preprint arXiv:2302.00923},
-  year={2023}
-}
-```
-
-## License
-
-This project is licensed under the Apache-2.0 License.
-
-## Acknowledgement
-
-Part of our codes are adapted from [ScienceQA](https://github.com/lupantech/ScienceQA) and [Transformers](https://github.com/huggingface/transformers).
-
-We thank Pan Lu for providing parameter size for ScienceQA baselines.

mm-cot/evaluations.py

@@ -1,100 +0,0 @@
-'''
-Adapted from https://github.com/lupantech/ScienceQA
-'''
-
-import re
-from rouge import Rouge
-from nltk.translate.bleu_score import sentence_bleu
-from sentence_transformers import util
-
-########################
-## BLEU
-########################
-def tokenize(text):
-    tokens = re.split(r'\s|\.', text)
-    tokens = [t for t in tokens if len(t) > 0]
-    return tokens
-
-
-def bleu_score(reference, hypothesis, gram):
-    reference_tokens = tokenize(reference)
-    hypothesis_tokens = tokenize(hypothesis)
-
-    if gram == 1:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1., ))  # BELU-1
-    elif gram == 2:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1. / 2., 1. / 2.))  # BELU-2
-    elif gram == 3:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1. / 3., 1. / 3., 1. / 3.))  # BELU-3
-    elif gram == 4:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1. / 4., 1. / 4., 1. / 4., 1. / 4.))  # BELU-4
-
-    return bleu
-
-
-def caculate_bleu(results, data, gram):
-    bleus = []
-    for qid, output in results.items():
-        prediction = output
-        target = data[qid]
-        target = target.strip()
-        if target == "":
-            continue
-        bleu = bleu_score(target, prediction, gram)
-        bleus.append(bleu)
-
-    avg_bleu = sum(bleus) / len(bleus)
-
-    return avg_bleu
-
-
-########################
-## Rouge-L
-########################
-def score_rouge(str1, str2):
-    rouge = Rouge(metrics=["rouge-l"])
-    scores = rouge.get_scores(str1, str2, avg=True)
-    rouge_l = scores['rouge-l']['f']
-    return rouge_l
-
-
-def caculate_rouge(results, data):
-    rouges = []
-    for qid, output in results.items():
-        prediction = output
-        target = data[qid]
-        target = target.strip()
-        if prediction == "":
-            continue
-        if target == "":
-            continue
-        rouge = score_rouge(target, prediction)
-        rouges.append(rouge)
-
-    avg_rouge = sum(rouges) / len(rouges)
-    return avg_rouge
-
-
-########################
-## Sentence Similarity
-########################
-def similariry_score(str1, str2, model):
-    # compute embedding for both lists
-    embedding_1 = model.encode(str1, convert_to_tensor=True)
-    embedding_2 = model.encode(str2, convert_to_tensor=True)
-    score = util.pytorch_cos_sim(embedding_1, embedding_2).item()
-    return score
-
-
-def caculate_similariry(results, data, model):
-    scores = []
-    for qid, output in results.items():
-        prediction = output
-        target = data[qid]
-        target = target.strip()
-
-        score = similariry_score(target, prediction, model)
-        scores.append(score)
-
-    avg_score = sum(scores) / len(scores)
-    return avg_score

mm-cot/main.py

@@ -1,383 +0,0 @@
-import os
-import numpy as np
-import torch
-import os
-import re
-import json
-import argparse
-import random
-from transformers import T5Tokenizer, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer, T5ForConditionalGeneration
-from model import T5ForConditionalGeneration, T5ForMultimodalGeneration
-from utils_data import img_shape, load_data_std, load_data_img, ScienceQADatasetStd, ScienceQADatasetImg
-from utils_prompt import *
-from utils_evaluate import get_scores
-from rich.table import Column, Table
-from rich import box
-from rich.console import Console
-console = Console(record=True)
-from torch import cuda
-import nltk
-import evaluate
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data_root', type=str, default='data')
-    parser.add_argument('--output_dir', type=str, default='experiments')
-    parser.add_argument('--model', type=str, default='allenai/unifiedqa-t5-base')
-    parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
-    parser.add_argument('--epoch', type=int, default=20)
-    parser.add_argument('--lr', type=float, default=5e-5)
-    parser.add_argument('--bs', type=int, default=16)
-    parser.add_argument('--input_len', type=int, default=512)
-    parser.add_argument('--output_len', type=int, default=64)
-    parser.add_argument('--eval_bs', type=int, default=16)
-    parser.add_argument('--eval_acc', type=int, default=None, help='evaluate accumulation step')
-    parser.add_argument('--train_split', type=str, default='train', choices=['train', 'trainval', 'minitrain'])
-    parser.add_argument('--val_split', type=str, default='val', choices=['test', 'val', 'minival'])
-    parser.add_argument('--test_split', type=str, default='test', choices=['test', 'minitest'])
-    
-    parser.add_argument('--use_generate', action='store_true', help='only for baseline to improve inference speed')
-    parser.add_argument('--final_eval', action='store_true', help='only evaluate the model at the final epoch')
-    parser.add_argument('--user_msg', type=str, default="baseline", help='experiment type in the save_dir')
-    parser.add_argument('--img_type', type=str, default=None, choices=['detr', 'clip', 'resnet'], help='type of image features')
-    parser.add_argument('--eval_le', type=str, default=None, help='generated rationale for the dev set')
-    parser.add_argument('--test_le', type=str, default=None, help='generated rationale for the test set')
-    parser.add_argument('--evaluate_dir', type=str, default=None, help='the directory of model for evaluation')
-    parser.add_argument('--caption_file', type=str, default='data/captions.json')
-    parser.add_argument('--use_caption', action='store_true', help='use image captions or not')
-    parser.add_argument('--prompt_format', type=str, default='QCM-A', help='prompt format template',
-                        choices=['QCM-A', 'QCM-LE', 'QCMG-A', 'QCM-LEA', 'QCM-ALE'])
-    parser.add_argument('--seed', type=int, default=42, help='random seed')
-
-    args = parser.parse_args()
-    return args
-        
-def T5Trainer(
-    dataframe, args,
-):
-    torch.manual_seed(args.seed)  # pytorch random seed
-    np.random.seed(args.seed)  # numpy random seed
-    torch.backends.cudnn.deterministic = True
-    
-    if args.evaluate_dir is not None:
-        args.model = args.evaluate_dir
-
-    tokenizer = T5Tokenizer.from_pretrained(args.model)
-
-    console.log(f"""[Model]: Loading {args.model}...\n""")
-    console.log(f"[Data]: Reading data...\n")
-    problems = dataframe['problems']
-    qids = dataframe['qids']
-    train_qids = qids['train']
-    test_qids = qids['test']
-    val_qids = qids['val']
-    
-    if args.evaluate_dir is not None:
-        save_dir = args.evaluate_dir
-    else:
-        model_name = args.model.replace("/","-")
-        gpu_count = torch.cuda.device_count()
-        save_dir = f"{args.output_dir}/{args.user_msg}_{model_name}_{args.img_type}_{args.prompt_format}_lr{args.lr}_bs{args.bs * gpu_count}_op{args.output_len}_ep{args.epoch}"
-        if not os.path.exists(save_dir):
-            os.mkdir(save_dir)
-
-    padding_idx = tokenizer._convert_token_to_id(tokenizer.pad_token)
-    if args.img_type is not None:
-        patch_size = img_shape[args.img_type]
-        model = T5ForMultimodalGeneration.from_pretrained(args.model, patch_size=patch_size, padding_idx=padding_idx, save_dir=save_dir) 
-        name_maps = dataframe['name_maps'] 
-        image_features = dataframe['image_features']
-        train_set = ScienceQADatasetImg(
-            problems,
-            train_qids,
-            name_maps,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            image_features,
-        )
-        eval_set = ScienceQADatasetImg(
-            problems,
-            val_qids,
-            name_maps,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            image_features,
-            args.eval_le,
-        )
-        test_set = ScienceQADatasetImg(
-            problems,
-            test_qids,
-            name_maps,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            image_features,
-            args.test_le,
-        )
-    else:
-        model = T5ForConditionalGeneration.from_pretrained(args.model) 
-        train_set = ScienceQADatasetStd(
-            problems,
-            train_qids,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-        )
-        eval_set = ScienceQADatasetStd(
-            problems,
-            val_qids,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            args.eval_le,
-        )
-        
-        test_set = ScienceQADatasetStd(
-            problems,
-            test_qids,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            args.test_le,
-        )
-
-    datacollator = DataCollatorForSeq2Seq(tokenizer)
-    print("model parameters: ", model.num_parameters())
-    def extract_ans(ans):
-        pattern = re.compile(r'The answer is \(([A-Z])\)')
-        res = pattern.findall(ans)
-        
-        if len(res) == 1:
-            answer = res[0]  # 'A', 'B', ...
-        else:
-            answer = "FAILED" 
-        return answer  
-
-    # accuracy for answer inference
-    def compute_metrics_acc(eval_preds):
-        if args.use_generate:
-            preds, targets = eval_preds
-            if isinstance(preds, tuple):
-                preds = preds[0]
-        else:
-            preds = eval_preds.predictions[0]
-            targets = eval_preds.label_ids
-            preds = preds.argmax(axis=2)
-        preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        targets = tokenizer.batch_decode(targets, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        correct = 0
-        assert len(preds) == len(targets)
-        for idx, pred in enumerate(preds):
-            reference = targets[idx]
-            reference = extract_ans(reference)
-            extract_pred = extract_ans(pred)
-            best_option = extract_pred
-            if reference == best_option:
-                correct +=1 
-        return {'accuracy': 1.0*correct/len(targets)}
-    
-    # rougel for rationale generation
-    metric = evaluate.load("rouge")
-    def postprocess_text(preds, labels):
-        preds = [pred.strip() for pred in preds]
-        labels = [label.strip() for label in labels]
-        preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
-        labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
-        return preds, labels
-
-    def compute_metrics_rougel(eval_preds):
-        if args.use_generate:
-            preds, targets = eval_preds
-            if isinstance(preds, tuple):
-                preds = preds[0]
-        else:
-            preds = eval_preds.predictions[0]
-            targets = eval_preds.label_ids
-            preds = preds.argmax(axis=2)
-        preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        targets = tokenizer.batch_decode(targets, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-
-        decoded_preds, decoded_labels = postprocess_text(preds, targets)
-
-        result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
-        result = {k: round(v * 100, 4) for k, v in result.items()}
-        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
-        result["gen_len"] = np.mean(prediction_lens)
-        return result
-
-    # only use the last model for evaluation to save time
-    if args.final_eval:
-        training_args = Seq2SeqTrainingArguments(
-            save_dir,
-            do_train=True if args.evaluate_dir is None else False,
-            do_eval=False,
-            evaluation_strategy="no",
-            logging_strategy="steps",
-            save_strategy="epoch",
-            save_total_limit = 2,
-            learning_rate= args.lr,
-            eval_accumulation_steps=args.eval_acc,
-            per_device_train_batch_size=args.bs,
-            per_device_eval_batch_size=args.eval_bs,
-            weight_decay=0.01,
-            num_train_epochs=args.epoch,
-            predict_with_generate=args.use_generate,
-            report_to="none",
-        )
-    # evaluate at each epoch
-    else:
-        training_args = Seq2SeqTrainingArguments(
-            save_dir,
-            do_train=True if args.evaluate_dir is None else False,
-            do_eval=True,
-            evaluation_strategy="epoch",
-            logging_strategy="steps",
-            save_strategy="epoch",
-            save_total_limit = 2,
-            learning_rate= args.lr,
-            eval_accumulation_steps=args.eval_acc,
-            per_device_train_batch_size=args.bs,
-            per_device_eval_batch_size=args.eval_bs,
-            weight_decay=0.01,
-            num_train_epochs=args.epoch,
-            metric_for_best_model="accuracy" if args.prompt_format != "QCM-LE" else "rougeL",
-            predict_with_generate=args.use_generate,
-            load_best_model_at_end=True,
-            report_to="none",
-        )
-
-    trainer = Seq2SeqTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_set,
-        eval_dataset=eval_set,
-        data_collator=datacollator,
-        tokenizer=tokenizer,
-        compute_metrics = compute_metrics_acc if args.prompt_format != "QCM-LE" else compute_metrics_rougel
-    )
-
-    if args.evaluate_dir is None:
-        trainer.train()
-        trainer.save_model(save_dir)
-        
-    metrics = trainer.evaluate(eval_dataset = test_set)
-    trainer.log_metrics("test", metrics)
-    trainer.save_metrics("test", metrics)
-
-    predict_results = trainer.predict(test_dataset=test_set, max_length=args.output_len) 
-    if trainer.is_world_process_zero():
-        if args.use_generate:
-            preds, targets = predict_results.predictions, predict_results.label_ids
-        else:
-            preds = predict_results.predictions[0]
-            targets = predict_results.label_ids
-            preds = preds.argmax(axis=2)
-
-        preds = tokenizer.batch_decode(
-            preds, skip_special_tokens=True, clean_up_tokenization_spaces=True
-        )
-        targets = tokenizer.batch_decode(
-            targets, skip_special_tokens=True, clean_up_tokenization_spaces=True
-        )
-
-        results_ans = {}
-        results_rationale = {}
-        results_reference = {}
-        
-        num_fail = 0
-        for idx, qid in enumerate(test_qids):
-            pred = preds[int(idx)]
-            ref = targets[int(idx)]
-            extract_pred = extract_ans(pred)
-            if extract_pred != "FAILED":
-                if extract_pred in args.options:
-                    extract_pred = args.options.index(extract_pred)
-                else:
-                    extract_pred = random.choice(range(0,len(args.options)))
-            else:
-                num_fail += 1
-                extract_pred = random.choice(range(len(args.options))) # random choose one option
-            results_ans[str(qid)] = extract_pred
-            results_rationale[str(qid)] = pred
-            results_reference[str(qid)] = ref
-
-        scores = get_scores(results_ans, results_rationale, results_reference, os.path.join(args.data_root, "scienceqa/problems.json"))
-        preds = [pred.strip() for pred in preds]
-        output_data = {
-                "num_fail": num_fail,
-                "scores": scores,
-                "preds": preds,
-                 "labels": targets}
-        output_prediction_file = os.path.join(save_dir,"predictions_ans_test.json")
-        with open(output_prediction_file, "w") as writer:
-            writer.write(json.dumps(output_data, indent=4))
-    
-    # generate the rationale for the eval set
-    if args.prompt_format == "QCM-LE":
-        torch.cuda.empty_cache()
-        del predict_results, preds, targets
-        predict_results = trainer.predict(test_dataset=eval_set, max_length=args.output_len) 
-        if trainer.is_world_process_zero():
-            if args.use_generate:
-                preds, targets = predict_results.predictions, predict_results.label_ids
-            else:
-                preds = predict_results.predictions[0]
-                targets = predict_results.label_ids
-                preds = preds.argmax(axis=2)
-
-            preds = tokenizer.batch_decode(
-                preds, skip_special_tokens=True, clean_up_tokenization_spaces=True
-            )
-            targets = tokenizer.batch_decode(
-                targets, skip_special_tokens=True, clean_up_tokenization_spaces=True
-            )
-            preds = [pred.strip() for pred in preds]
-            output_data = {"preds": preds,
-                 "labels": targets}
-            output_prediction_file = os.path.join(save_dir,"predictions_ans_eval.json")
-            with open(output_prediction_file, "w") as writer:
-                writer.write(json.dumps(output_data, indent=4))
-    
-
-if __name__ == '__main__':
-
-    # training logger to log training progress
-    training_logger = Table(
-        Column("Epoch", justify="center"),
-        Column("Steps", justify="center"),
-        Column("Loss", justify="center"),
-        title="Training Status",
-        pad_edge=False,
-        box=box.ASCII,
-    )
-    
-    args = parse_args()
-    print("args",args)
-    print('====Input Arguments====')
-    print(json.dumps(vars(args), indent=2, sort_keys=False))
-
-    random.seed(args.seed)
-    
-    if not os.path.exists(args.output_dir):
-            os.mkdir(args.output_dir)
-
-    if args.img_type is not None:
-        problems, qids, name_maps, image_features = load_data_img(args)  # probelms, test question ids, shot example ids
-        dataframe = {'problems':problems, 'qids':qids, 'name_maps': name_maps, 'image_features': image_features}
-    else:
-        problems, qids = load_data_std(args)  # probelms, test question ids, shot example ids
-        dataframe = {'problems':problems, 'qids':qids}
-
-    T5Trainer(
-        dataframe=dataframe,
-        args = args
-    )

mm-cot/mm-cot/CODE_OF_CONDUCT.md

@@ -1,4 +0,0 @@
-## Code of Conduct
-This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
-For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
-opensource-codeofconduct@amazon.com with any additional questions or comments.

mm-cot/mm-cot/CONTRIBUTING.md

@@ -1,59 +0,0 @@
-# Contributing Guidelines
-
-Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
-documentation, we greatly value feedback and contributions from our community.
-
-Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
-information to effectively respond to your bug report or contribution.
-
-
-## Reporting Bugs/Feature Requests
-
-We welcome you to use the GitHub issue tracker to report bugs or suggest features.
-
-When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already
-reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
-
-* A reproducible test case or series of steps
-* The version of our code being used
-* Any modifications you've made relevant to the bug
-* Anything unusual about your environment or deployment
-
-
-## Contributing via Pull Requests
-Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
-
-1. You are working against the latest source on the *main* branch.
-2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.
-3. You open an issue to discuss any significant work - we would hate for your time to be wasted.
-
-To send us a pull request, please:
-
-1. Fork the repository.
-2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
-3. Ensure local tests pass.
-4. Commit to your fork using clear commit messages.
-5. Send us a pull request, answering any default questions in the pull request interface.
-6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
-
-GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and
-[creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
-
-
-## Finding contributions to work on
-Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.
-
-
-## Code of Conduct
-This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
-For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
-opensource-codeofconduct@amazon.com with any additional questions or comments.
-
-
-## Security issue notifications
-If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
-
-
-## Licensing
-
-See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.

mm-cot/mm-cot/LICENSE

@@ -1,175 +0,0 @@
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-      outstanding shares, or (iii) beneficial ownership of such entity.
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-
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-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
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-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
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-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
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mm-cot/mm-cot/NOTICE

@@ -1 +0,0 @@
-Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

mm-cot/mm-cot/README.md

@@ -1,93 +0,0 @@
-# Multimodal Chain-of-Thought Reasoning in Language Models
-
-<h5 align="center"><i>"Imagine learning a textbook without figures or tables."</i></h5>
-
-Multimodal-CoT incorporates vision features in a decoupled training framework. The framework consists of two training stages: (i) rationale generation and (ii) answer inference. Both stages share the same model architecture but differ in the input and output.
-
-![](vision_features/mm-cot.png)
-
-
-## Requirements
-
-Install all required python dependencies:
-
-```
-pip install -r requirements.txt
-```
-
-## Datasets
-
-Download the dataset from the following repository:
-
-```
-https://github.com/lupantech/ScienceQA/tree/main/data
-```
-
-Download the extracted vision features from [vision_features](https://drive.google.com/file/d/13B0hc_F_45-UlqPLKSgRz-ALtFQ8kIJr/view?usp=share_link) and unzip the files under `vision_features`
-
-## Instructions
-
-### Training 
-
-```
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_eval.json \
-    --test_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_test.json
-```
-
-### Inference 
-
-Our trained models are available at [models](https://drive.google.com/file/d/1FtTYOJPHnWnFfCxNC6M3gar4RAX5E21b/view?usp=share_link). To use our trained models, please put the them under the ```models``` folder.
-
-```
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE \
-    --evaluate_dir models/MM-CoT-UnifiedQA-base-Rationale
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le models/rationale/predictions_ans_eval.json \
-    --test_le models/rationale/predictions_ans_test.json \
-    --evaluate_dir models/MM-CoT-UnifiedQA-base-Answer
-```
-
-## Citing MM-CoT
-
-```
-@article{zhang2023multicot,
-  title={Multimodal Chain-of-Thought Reasoning in Language Models},
-  author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Zhao, Hai and Karypis, George and Smola, Alex},
-  journal={arXiv preprint arXiv:2302.00923},
-  year={2023}
-}
-```
-
-## License
-
-This project is licensed under the Apache-2.0 License.
-
-## Acknowledgement
-
-Part of our codes are adapted from [ScienceQA](https://github.com/lupantech/ScienceQA) and [Transformers](https://github.com/huggingface/transformers).
-
-We thank Pan Lu for providing parameter size for ScienceQA baselines.

mm-cot/mm-cot/evaluations.py

@@ -1,100 +0,0 @@
-'''
-Adapted from https://github.com/lupantech/ScienceQA
-'''
-
-import re
-from rouge import Rouge
-from nltk.translate.bleu_score import sentence_bleu
-from sentence_transformers import util
-
-########################
-## BLEU
-########################
-def tokenize(text):
-    tokens = re.split(r'\s|\.', text)
-    tokens = [t for t in tokens if len(t) > 0]
-    return tokens
-
-
-def bleu_score(reference, hypothesis, gram):
-    reference_tokens = tokenize(reference)
-    hypothesis_tokens = tokenize(hypothesis)
-
-    if gram == 1:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1., ))  # BELU-1
-    elif gram == 2:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1. / 2., 1. / 2.))  # BELU-2
-    elif gram == 3:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1. / 3., 1. / 3., 1. / 3.))  # BELU-3
-    elif gram == 4:
-        bleu = sentence_bleu([reference_tokens], hypothesis_tokens, (1. / 4., 1. / 4., 1. / 4., 1. / 4.))  # BELU-4
-
-    return bleu
-
-
-def caculate_bleu(results, data, gram):
-    bleus = []
-    for qid, output in results.items():
-        prediction = output
-        target = data[qid]
-        target = target.strip()
-        if target == "":
-            continue
-        bleu = bleu_score(target, prediction, gram)
-        bleus.append(bleu)
-
-    avg_bleu = sum(bleus) / len(bleus)
-
-    return avg_bleu
-
-
-########################
-## Rouge-L
-########################
-def score_rouge(str1, str2):
-    rouge = Rouge(metrics=["rouge-l"])
-    scores = rouge.get_scores(str1, str2, avg=True)
-    rouge_l = scores['rouge-l']['f']
-    return rouge_l
-
-
-def caculate_rouge(results, data):
-    rouges = []
-    for qid, output in results.items():
-        prediction = output
-        target = data[qid]
-        target = target.strip()
-        if prediction == "":
-            continue
-        if target == "":
-            continue
-        rouge = score_rouge(target, prediction)
-        rouges.append(rouge)
-
-    avg_rouge = sum(rouges) / len(rouges)
-    return avg_rouge
-
-
-########################
-## Sentence Similarity
-########################
-def similariry_score(str1, str2, model):
-    # compute embedding for both lists
-    embedding_1 = model.encode(str1, convert_to_tensor=True)
-    embedding_2 = model.encode(str2, convert_to_tensor=True)
-    score = util.pytorch_cos_sim(embedding_1, embedding_2).item()
-    return score
-
-
-def caculate_similariry(results, data, model):
-    scores = []
-    for qid, output in results.items():
-        prediction = output
-        target = data[qid]
-        target = target.strip()
-
-        score = similariry_score(target, prediction, model)
-        scores.append(score)
-
-    avg_score = sum(scores) / len(scores)
-    return avg_score

mm-cot/mm-cot/main.py

@@ -1,383 +0,0 @@
-import os
-import numpy as np
-import torch
-import os
-import re
-import json
-import argparse
-import random
-from transformers import T5Tokenizer, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer, T5ForConditionalGeneration
-from model import T5ForConditionalGeneration, T5ForMultimodalGeneration
-from utils_data import img_shape, load_data_std, load_data_img, ScienceQADatasetStd, ScienceQADatasetImg
-from utils_prompt import *
-from utils_evaluate import get_scores
-from rich.table import Column, Table
-from rich import box
-from rich.console import Console
-console = Console(record=True)
-from torch import cuda
-import nltk
-import evaluate
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data_root', type=str, default='data')
-    parser.add_argument('--output_dir', type=str, default='experiments')
-    parser.add_argument('--model', type=str, default='allenai/unifiedqa-t5-base')
-    parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
-    parser.add_argument('--epoch', type=int, default=20)
-    parser.add_argument('--lr', type=float, default=5e-5)
-    parser.add_argument('--bs', type=int, default=16)
-    parser.add_argument('--input_len', type=int, default=512)
-    parser.add_argument('--output_len', type=int, default=64)
-    parser.add_argument('--eval_bs', type=int, default=16)
-    parser.add_argument('--eval_acc', type=int, default=None, help='evaluate accumulation step')
-    parser.add_argument('--train_split', type=str, default='train', choices=['train', 'trainval', 'minitrain'])
-    parser.add_argument('--val_split', type=str, default='val', choices=['test', 'val', 'minival'])
-    parser.add_argument('--test_split', type=str, default='test', choices=['test', 'minitest'])
-    
-    parser.add_argument('--use_generate', action='store_true', help='only for baseline to improve inference speed')
-    parser.add_argument('--final_eval', action='store_true', help='only evaluate the model at the final epoch')
-    parser.add_argument('--user_msg', type=str, default="baseline", help='experiment type in the save_dir')
-    parser.add_argument('--img_type', type=str, default=None, choices=['detr', 'clip', 'resnet'], help='type of image features')
-    parser.add_argument('--eval_le', type=str, default=None, help='generated rationale for the dev set')
-    parser.add_argument('--test_le', type=str, default=None, help='generated rationale for the test set')
-    parser.add_argument('--evaluate_dir', type=str, default=None, help='the directory of model for evaluation')
-    parser.add_argument('--caption_file', type=str, default='data/captions.json')
-    parser.add_argument('--use_caption', action='store_true', help='use image captions or not')
-    parser.add_argument('--prompt_format', type=str, default='QCM-A', help='prompt format template',
-                        choices=['QCM-A', 'QCM-LE', 'QCMG-A', 'QCM-LEA', 'QCM-ALE'])
-    parser.add_argument('--seed', type=int, default=42, help='random seed')
-
-    args = parser.parse_args()
-    return args
-        
-def T5Trainer(
-    dataframe, args,
-):
-    torch.manual_seed(args.seed)  # pytorch random seed
-    np.random.seed(args.seed)  # numpy random seed
-    torch.backends.cudnn.deterministic = True
-    
-    if args.evaluate_dir is not None:
-        args.model = args.evaluate_dir
-
-    tokenizer = T5Tokenizer.from_pretrained(args.model)
-
-    console.log(f"""[Model]: Loading {args.model}...\n""")
-    console.log(f"[Data]: Reading data...\n")
-    problems = dataframe['problems']
-    qids = dataframe['qids']
-    train_qids = qids['train']
-    test_qids = qids['test']
-    val_qids = qids['val']
-    
-    if args.evaluate_dir is not None:
-        save_dir = args.evaluate_dir
-    else:
-        model_name = args.model.replace("/","-")
-        gpu_count = torch.cuda.device_count()
-        save_dir = f"{args.output_dir}/{args.user_msg}_{model_name}_{args.img_type}_{args.prompt_format}_lr{args.lr}_bs{args.bs * gpu_count}_op{args.output_len}_ep{args.epoch}"
-        if not os.path.exists(save_dir):
-            os.mkdir(save_dir)
-
-    padding_idx = tokenizer._convert_token_to_id(tokenizer.pad_token)
-    if args.img_type is not None:
-        patch_size = img_shape[args.img_type]
-        model = T5ForMultimodalGeneration.from_pretrained(args.model, patch_size=patch_size, padding_idx=padding_idx, save_dir=save_dir) 
-        name_maps = dataframe['name_maps'] 
-        image_features = dataframe['image_features']
-        train_set = ScienceQADatasetImg(
-            problems,
-            train_qids,
-            name_maps,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            image_features,
-        )
-        eval_set = ScienceQADatasetImg(
-            problems,
-            val_qids,
-            name_maps,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            image_features,
-            args.eval_le,
-        )
-        test_set = ScienceQADatasetImg(
-            problems,
-            test_qids,
-            name_maps,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            image_features,
-            args.test_le,
-        )
-    else:
-        model = T5ForConditionalGeneration.from_pretrained(args.model) 
-        train_set = ScienceQADatasetStd(
-            problems,
-            train_qids,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-        )
-        eval_set = ScienceQADatasetStd(
-            problems,
-            val_qids,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            args.eval_le,
-        )
-        
-        test_set = ScienceQADatasetStd(
-            problems,
-            test_qids,
-            tokenizer,
-            args.input_len,
-            args.output_len,
-            args,
-            args.test_le,
-        )
-
-    datacollator = DataCollatorForSeq2Seq(tokenizer)
-    print("model parameters: ", model.num_parameters())
-    def extract_ans(ans):
-        pattern = re.compile(r'The answer is \(([A-Z])\)')
-        res = pattern.findall(ans)
-        
-        if len(res) == 1:
-            answer = res[0]  # 'A', 'B', ...
-        else:
-            answer = "FAILED" 
-        return answer  
-
-    # accuracy for answer inference
-    def compute_metrics_acc(eval_preds):
-        if args.use_generate:
-            preds, targets = eval_preds
-            if isinstance(preds, tuple):
-                preds = preds[0]
-        else:
-            preds = eval_preds.predictions[0]
-            targets = eval_preds.label_ids
-            preds = preds.argmax(axis=2)
-        preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        targets = tokenizer.batch_decode(targets, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        correct = 0
-        assert len(preds) == len(targets)
-        for idx, pred in enumerate(preds):
-            reference = targets[idx]
-            reference = extract_ans(reference)
-            extract_pred = extract_ans(pred)
-            best_option = extract_pred
-            if reference == best_option:
-                correct +=1 
-        return {'accuracy': 1.0*correct/len(targets)}
-    
-    # rougel for rationale generation
-    metric = evaluate.load("rouge")
-    def postprocess_text(preds, labels):
-        preds = [pred.strip() for pred in preds]
-        labels = [label.strip() for label in labels]
-        preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
-        labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
-        return preds, labels
-
-    def compute_metrics_rougel(eval_preds):
-        if args.use_generate:
-            preds, targets = eval_preds
-            if isinstance(preds, tuple):
-                preds = preds[0]
-        else:
-            preds = eval_preds.predictions[0]
-            targets = eval_preds.label_ids
-            preds = preds.argmax(axis=2)
-        preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        targets = tokenizer.batch_decode(targets, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-
-        decoded_preds, decoded_labels = postprocess_text(preds, targets)
-
-        result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
-        result = {k: round(v * 100, 4) for k, v in result.items()}
-        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
-        result["gen_len"] = np.mean(prediction_lens)
-        return result
-
-    # only use the last model for evaluation to save time
-    if args.final_eval:
-        training_args = Seq2SeqTrainingArguments(
-            save_dir,
-            do_train=True if args.evaluate_dir is None else False,
-            do_eval=False,
-            evaluation_strategy="no",
-            logging_strategy="steps",
-            save_strategy="epoch",
-            save_total_limit = 2,
-            learning_rate= args.lr,
-            eval_accumulation_steps=args.eval_acc,
-            per_device_train_batch_size=args.bs,
-            per_device_eval_batch_size=args.eval_bs,
-            weight_decay=0.01,
-            num_train_epochs=args.epoch,
-            predict_with_generate=args.use_generate,
-            report_to="none",
-        )
-    # evaluate at each epoch
-    else:
-        training_args = Seq2SeqTrainingArguments(
-            save_dir,
-            do_train=True if args.evaluate_dir is None else False,
-            do_eval=True,
-            evaluation_strategy="epoch",
-            logging_strategy="steps",
-            save_strategy="epoch",
-            save_total_limit = 2,
-            learning_rate= args.lr,
-            eval_accumulation_steps=args.eval_acc,
-            per_device_train_batch_size=args.bs,
-            per_device_eval_batch_size=args.eval_bs,
-            weight_decay=0.01,
-            num_train_epochs=args.epoch,
-            metric_for_best_model="accuracy" if args.prompt_format != "QCM-LE" else "rougeL",
-            predict_with_generate=args.use_generate,
-            load_best_model_at_end=True,
-            report_to="none",
-        )
-
-    trainer = Seq2SeqTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_set,
-        eval_dataset=eval_set,
-        data_collator=datacollator,
-        tokenizer=tokenizer,
-        compute_metrics = compute_metrics_acc if args.prompt_format != "QCM-LE" else compute_metrics_rougel
-    )
-
-    if args.evaluate_dir is None:
-        trainer.train()
-        trainer.save_model(save_dir)
-        
-    metrics = trainer.evaluate(eval_dataset = test_set)
-    trainer.log_metrics("test", metrics)
-    trainer.save_metrics("test", metrics)
-
-    predict_results = trainer.predict(test_dataset=test_set, max_length=args.output_len) 
-    if trainer.is_world_process_zero():
-        if args.use_generate:
-            preds, targets = predict_results.predictions, predict_results.label_ids
-        else:
-            preds = predict_results.predictions[0]
-            targets = predict_results.label_ids
-            preds = preds.argmax(axis=2)
-
-        preds = tokenizer.batch_decode(
-            preds, skip_special_tokens=True, clean_up_tokenization_spaces=True
-        )
-        targets = tokenizer.batch_decode(
-            targets, skip_special_tokens=True, clean_up_tokenization_spaces=True
-        )
-
-        results_ans = {}
-        results_rationale = {}
-        results_reference = {}
-        
-        num_fail = 0
-        for idx, qid in enumerate(test_qids):
-            pred = preds[int(idx)]
-            ref = targets[int(idx)]
-            extract_pred = extract_ans(pred)
-            if extract_pred != "FAILED":
-                if extract_pred in args.options:
-                    extract_pred = args.options.index(extract_pred)
-                else:
-                    extract_pred = random.choice(range(0,len(args.options)))
-            else:
-                num_fail += 1
-                extract_pred = random.choice(range(len(args.options))) # random choose one option
-            results_ans[str(qid)] = extract_pred
-            results_rationale[str(qid)] = pred
-            results_reference[str(qid)] = ref
-
-        scores = get_scores(results_ans, results_rationale, results_reference, os.path.join(args.data_root, "scienceqa/problems.json"))
-        preds = [pred.strip() for pred in preds]
-        output_data = {
-                "num_fail": num_fail,
-                "scores": scores,
-                "preds": preds,
-                 "labels": targets}
-        output_prediction_file = os.path.join(save_dir,"predictions_ans_test.json")
-        with open(output_prediction_file, "w") as writer:
-            writer.write(json.dumps(output_data, indent=4))
-    
-    # generate the rationale for the eval set
-    if args.prompt_format == "QCM-LE":
-        torch.cuda.empty_cache()
-        del predict_results, preds, targets
-        predict_results = trainer.predict(test_dataset=eval_set, max_length=args.output_len) 
-        if trainer.is_world_process_zero():
-            if args.use_generate:
-                preds, targets = predict_results.predictions, predict_results.label_ids
-            else:
-                preds = predict_results.predictions[0]
-                targets = predict_results.label_ids
-                preds = preds.argmax(axis=2)
-
-            preds = tokenizer.batch_decode(
-                preds, skip_special_tokens=True, clean_up_tokenization_spaces=True
-            )
-            targets = tokenizer.batch_decode(
-                targets, skip_special_tokens=True, clean_up_tokenization_spaces=True
-            )
-            preds = [pred.strip() for pred in preds]
-            output_data = {"preds": preds,
-                 "labels": targets}
-            output_prediction_file = os.path.join(save_dir,"predictions_ans_eval.json")
-            with open(output_prediction_file, "w") as writer:
-                writer.write(json.dumps(output_data, indent=4))
-    
-
-if __name__ == '__main__':
-
-    # training logger to log training progress
-    training_logger = Table(
-        Column("Epoch", justify="center"),
-        Column("Steps", justify="center"),
-        Column("Loss", justify="center"),
-        title="Training Status",
-        pad_edge=False,
-        box=box.ASCII,
-    )
-    
-    args = parse_args()
-    print("args",args)
-    print('====Input Arguments====')
-    print(json.dumps(vars(args), indent=2, sort_keys=False))
-
-    random.seed(args.seed)
-    
-    if not os.path.exists(args.output_dir):
-            os.mkdir(args.output_dir)
-
-    if args.img_type is not None:
-        problems, qids, name_maps, image_features = load_data_img(args)  # probelms, test question ids, shot example ids
-        dataframe = {'problems':problems, 'qids':qids, 'name_maps': name_maps, 'image_features': image_features}
-    else:
-        problems, qids = load_data_std(args)  # probelms, test question ids, shot example ids
-        dataframe = {'problems':problems, 'qids':qids}
-
-    T5Trainer(
-        dataframe=dataframe,
-        args = args
-    )

mm-cot/mm-cot/model.py

@@ -1,194 +0,0 @@
-'''
-Adapted from https://github.com/huggingface/transformers
-'''
-
-from transformers import T5Config, T5ForConditionalGeneration
-from transformers.models.t5.modeling_t5 import T5Stack, __HEAD_MASK_WARNING_MSG, T5EncoderModel
-import copy
-import math
-import os
-import warnings
-from typing import Optional, Tuple, Union
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers.modeling_outputs import (
-    BaseModelOutput,
-    Seq2SeqLMOutput,
-)
-
-class T5ForMultimodalGeneration(T5ForConditionalGeneration):
-    _keys_to_ignore_on_load_missing = [
-        r"encoder.embed_tokens.weight",
-        r"decoder.embed_tokens.weight",
-        r"lm_head.weight",
-    ]
-    _keys_to_ignore_on_load_unexpected = [
-        r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
-    ]
-
-    def __init__(self, config: T5Config, patch_size, padding_idx, save_dir):
-        super().__init__(config)
-        self.model_dim = config.d_model
-        
-        self.padding_idx = padding_idx
-        self.out = open(os.path.join(save_dir, 'gate.txt'), 'w')
-
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-        self.patch_num, self.patch_dim = patch_size
-
-        self.image_dense = nn.Linear(self.patch_dim, config.d_model)
-        self.mha_layer = torch.nn.MultiheadAttention(embed_dim=config.hidden_size, kdim=config.hidden_size, vdim=config.hidden_size, num_heads=1, batch_first=True)
-        self.gate_dense = nn.Linear(2*config.hidden_size, config.hidden_size)
-        self.sigmoid = nn.Sigmoid()
-
-        encoder_config = copy.deepcopy(config)
-        encoder_config.is_decoder = False
-        encoder_config.use_cache = False
-        encoder_config.is_encoder_decoder = False
-        self.encoder = T5Stack(encoder_config, self.shared)
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        decoder_config.is_encoder_decoder = False
-        decoder_config.num_layers = config.num_decoder_layers
-        self.decoder = T5Stack(decoder_config, self.shared)
-
-        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        # Model parallel
-        self.model_parallel = False
-        self.device_map = None
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        image_ids=None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        decoder_head_mask: Optional[torch.FloatTensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
-        if head_mask is not None and decoder_head_mask is None:
-            if self.config.num_layers == self.config.num_decoder_layers:
-                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
-                decoder_head_mask = head_mask
-
-        # Encode if needed (training, first prediction pass)
-        if encoder_outputs is None:
-            # Convert encoder inputs in embeddings if needed
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                inputs_embeds=inputs_embeds,
-                head_mask=head_mask,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-
-        hidden_states = encoder_outputs[0]
-        
-        image_embedding = self.image_dense(image_ids)
-        image_att, _ = self.mha_layer(hidden_states, image_embedding, image_embedding)
-
-        merge = torch.cat([hidden_states, image_att], dim=-1)
-        gate = self.sigmoid(self.gate_dense(merge))
-        hidden_states = (1 - gate) * hidden_states + gate * image_att
-
-        if self.model_parallel:
-            torch.cuda.set_device(self.decoder.first_device)
-
-        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
-            # get decoder inputs from shifting lm labels to the right
-            decoder_input_ids = self._shift_right(labels)
-
-        # Set device for model parallelism
-        if self.model_parallel:
-            torch.cuda.set_device(self.decoder.first_device)
-            hidden_states = hidden_states.to(self.decoder.first_device)
-            if decoder_input_ids is not None:
-                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
-            if attention_mask is not None:
-                attention_mask = attention_mask.to(self.decoder.first_device)
-            if decoder_attention_mask is not None:
-                decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
-
-        # Decode
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            inputs_embeds=decoder_inputs_embeds,
-            past_key_values=past_key_values,
-            encoder_hidden_states=hidden_states,
-            encoder_attention_mask=attention_mask,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = decoder_outputs[0]
-
-        # Set device for model parallelism
-        if self.model_parallel:
-            torch.cuda.set_device(self.encoder.first_device)
-            self.lm_head = self.lm_head.to(self.encoder.first_device)
-            sequence_output = sequence_output.to(self.lm_head.weight.device)
-
-        if self.config.tie_word_embeddings:
-            # Rescale output before projecting on vocab
-            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
-            sequence_output = sequence_output * (self.model_dim**-0.5)
-
-        lm_logits = self.lm_head(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss(ignore_index=-100)
-            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
-            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
-
-        if not return_dict:
-            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
-            return ((loss,) + output) if loss is not None else output
-
-        return Seq2SeqLMOutput(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )

mm-cot/mm-cot/requirements.txt

@@ -1,11 +0,0 @@
-huggingface-hub==0.0.12
-numpy==1.23.2
-openai==0.23.0
-pandas==1.4.3
-rouge==1.0.1
-sentence-transformers==2.2.2
-transformers==4.21.1
-nltk==3.6.6
-evaluate==0.4.0
-rouge==1.0.1
-rouge_score==0.1.2

mm-cot/mm-cot/run_inference.sh

@@ -1,17 +0,0 @@
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE \
-    --evaluate_dir models/rationale
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le models/rationale/predictions_ans_eval.json \
-    --test_le models/rationale/predictions_ans_test.json \
-    --evaluate_dir models/answer

mm-cot/mm-cot/run_training.sh

@@ -1,15 +0,0 @@
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_eval.json \
-    --test_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_test.json

mm-cot/mm-cot/utils_data.py

@@ -1,228 +0,0 @@
-import os
-from torch.utils.data import Dataset
-import os
-import json
-import numpy as np
-import torch
-from utils_prompt import *
-
-img_shape = {
-    "resnet": (512, 2048),
-    "clip": (49, 2048),
-    "detr": (100, 256),
-}
-
-def load_data_std(args):
-    problems = json.load(open(os.path.join(args.data_root, 'scienceqa/problems.json')))
-    pid_splits = json.load(open(os.path.join(args.data_root, 'scienceqa/pid_splits.json')))
-    captions = json.load(open(args.caption_file))["captions"]
-
-    for qid in problems:
-        problems[qid]['caption'] = captions[qid] if qid in captions else ""
-
-    train_qids = pid_splits['%s' % (args.train_split)]
-    val_qids = pid_splits['%s' % (args.val_split)]
-    test_qids = pid_splits['%s' % (args.test_split)]
-    print(f"number of train problems: {len(train_qids)}\n")
-    print(f"number of val problems: {len(val_qids)}\n")
-    print(f"number of test problems: {len(test_qids)}\n")
-
-    qids = {'train': train_qids, 'val':val_qids,'test':test_qids}
-    return problems, qids,
-
-def load_data_img(args):
-    problems = json.load(open(os.path.join(args.data_root, 'scienceqa/problems.json')))
-    pid_splits = json.load(open(os.path.join(args.data_root, 'scienceqa/pid_splits.json')))
-    captions = json.load(open(args.caption_file))["captions"]
-    name_maps = json.load(open('vision_features/name_map.json'))
-
-    # check
-    if args.img_type == "resnet":
-        image_features = np.load('vision_features/resnet.npy')
-        image_features = np.expand_dims(image_features, axis=1)
-        image_features = image_features.repeat(512, axis=1)
-    elif args.img_type == "clip":
-        image_features = np.load('vision_features/clip.npy')
-    elif args.img_type == "detr":
-        image_features = np.load('vision_features/detr.npy')
-    else:
-        image_features = np.load('vision_features/detr.npy')
-    print("img_features size: ", image_features.shape)
-
-    for qid in problems:
-        problems[qid]['caption'] = captions[qid] if qid in captions else ""
-
-    train_qids = pid_splits['%s' % (args.train_split)]
-    val_qids = pid_splits['%s' % (args.val_split)]
-    test_qids = pid_splits['%s' % (args.test_split)]
-    print(f"number of train problems: {len(train_qids)}\n")
-    print(f"number of val problems: {len(val_qids)}\n")
-    print(f"number of test problems: {len(test_qids)}\n")
-
-    qids = {'train': train_qids, 'val':val_qids,'test':test_qids}
-    return problems, qids, name_maps, image_features
-
-class ScienceQADatasetStd(Dataset):
-    """
-    Creating a custom dataset for reading the dataset and
-    loading it into the dataloader to pass it to the
-    neural network for finetuning the model
-
-    """
-
-    def __init__(
-        self, problems, qids, tokenizer, source_len, target_len, args, test_le=None
-    ):
-        self.tokenizer = tokenizer
-        self.data = {qid : problems[qid] for qid in qids}
-        self.source_len = source_len
-        self.summ_len = target_len
-        self.target_text = []
-        self.source_text = []
-        if test_le is not None:
-            test_le_data =json.load(open(test_le))["preds"]
-        else:
-            test_le_data = None
-        idx = 0
-        for qid in self.data:
-            if test_le_data is not None:
-                curr_le_data = test_le_data[idx]
-                idx += 1
-            else:
-                curr_le_data = None
-            prompt, target = build_train_pair(problems, qid, args, curr_le_data)
-            self.target_text.append(target)
-            self.source_text.append(prompt)
-
-    def __len__(self):
-        return len(self.target_text)
-
-    def __getitem__(self, index):
-        source_text = str(self.source_text[index])
-        target_text = str(self.target_text[index])
-
-        # cleaning data so as to ensure data is in string type
-        source_text = " ".join(source_text.split())
-        target_text = " ".join(target_text.split())
-
-        source = self.tokenizer.batch_encode_plus(
-            [source_text],
-            max_length=self.source_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        target = self.tokenizer.batch_encode_plus(
-            [target_text],
-            max_length=self.summ_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        source_ids = source["input_ids"].squeeze()
-        source_mask = source["attention_mask"].squeeze()
-        target_ids = target["input_ids"].squeeze().tolist()
-        
-        return {
-            "input_ids": source_ids,
-            "attention_mask": source_mask,
-            "labels": target_ids,
-        }
-
-
-class ScienceQADatasetImg(Dataset):
-    """
-    Creating a custom dataset for reading the dataset and
-    loading it into the dataloader to pass it to the
-    neural network for finetuning the model
-
-    """
-
-    def __init__(
-        self, problems, qids, name_maps, tokenizer, source_len, target_len, args, image_features, test_le=None
-    ):
-        """
-        Initializes a Dataset class
-
-        Args:
-            dataframe (pandas.DataFrame): Input dataframe
-            tokenizer (transformers.tokenizer): Transformers tokenizer
-            source_len (int): Max length of source text
-            target_len (int): Max length of target text
-            source_text (str): column name of source text
-            target_text (str): column name of target text
-        """
-        self.tokenizer = tokenizer
-        self.data = {qid : problems[qid] for qid in qids}
-        self.source_len = source_len
-        self.summ_len = target_len
-        self.target_text = []
-        self.source_text = []
-        self.image_ids = []
-        if test_le is not None:
-            test_le_data =json.load(open(test_le))["preds"]
-        else:
-            test_le_data = None
-        idx = 0
-        for qid in self.data:
-            if test_le_data is not None:
-                curr_le_data = test_le_data[idx]
-                idx += 1
-            else:
-                curr_le_data = None
-            prompt, target = build_train_pair(problems, qid, args, curr_le_data)
-            self.target_text.append(target)
-            self.source_text.append(prompt)
-            if str(qid) in name_maps:
-                i_vectors = image_features[int(name_maps[str(qid)])]
-                self.image_ids.append(i_vectors)
-            else:
-                shape = img_shape[args.img_type]
-                self.image_ids.append(np.zeros(shape))
-    
-    def __len__(self):
-        """returns the length of dataframe"""
-
-        return len(self.target_text)
-
-    def __getitem__(self, index):
-        """return the input ids, attention masks and target ids"""
-
-        source_text = str(self.source_text[index])
-        target_text = str(self.target_text[index])
-        image_ids = self.image_ids[index]
-
-        # cleaning data so as to ensure data is in string type
-        source_text = " ".join(source_text.split())
-        target_text = " ".join(target_text.split())
-
-        source = self.tokenizer.batch_encode_plus(
-            [source_text],
-            max_length=self.source_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        target = self.tokenizer.batch_encode_plus(
-            [target_text],
-            max_length=self.summ_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        source_ids = source["input_ids"].squeeze()
-        source_mask = source["attention_mask"].squeeze()
-        target_ids = target["input_ids"].squeeze().tolist()
-
-        image_ids = torch.tensor(image_ids).squeeze()
-        
-        return {
-            "input_ids": source_ids,
-            "attention_mask": source_mask,
-            "image_ids": image_ids,
-            "labels": target_ids,
-        }

mm-cot/mm-cot/utils_evaluate.py

@@ -1,108 +0,0 @@
-'''
-Adapted from https://github.com/lupantech/ScienceQA
-'''
-
-import os
-import json
-import argparse
-import warnings
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-from evaluations import caculate_bleu, caculate_rouge, caculate_similariry
-
-warnings.filterwarnings('ignore')
-
-def get_acc_with_contion(res_pd, key, values):
-    if isinstance(values, list):
-        total_pd = res_pd[res_pd[key].isin(values)]
-    else:
-        total_pd = res_pd[res_pd[key] == values]
-    correct_pd = total_pd[total_pd['true_false'] == True]
-    acc = "{:.2f}".format(len(correct_pd) / len(total_pd) * 100)
-    return acc
-
-
-def get_scores(result_data, rationale_data, results_reference, data_file):
-    # read result file
-    results = result_data
-    num = len(results)
-    assert num == 4241
-    #print("number of questions:", num)
-
-    # read data file
-    sqa_data = json.load(open(data_file))
-
-    # construct pandas data
-    sqa_pd = pd.DataFrame(sqa_data).T
-    res_pd = sqa_pd[sqa_pd['split'] == 'test']  # test set
-
-    # update data
-    for index, row in res_pd.iterrows():
-
-        res_pd.loc[index, 'no_context'] = True if (not row['hint'] and not row['image']) else False
-        res_pd.loc[index, 'has_text'] = True if row['hint'] else False
-        res_pd.loc[index, 'has_image'] = True if row['image'] else False
-        res_pd.loc[index, 'has_text_image'] = True if (row['hint'] and row['image']) else False
-
-        label = row['answer']
-        pred = int(results[index])
-        res_pd.loc[index, 'pred'] = pred
-        res_pd.loc[index, 'true_false'] = (label == pred)
-
-    # accuracy scores
-    acc_average = len(res_pd[res_pd['true_false'] == True]) / num * 100
-    #assert result_file.split('_')[-1] == "{:.3f}.json".format(acc_average)
-
-
-    # rationale quality
-
-    ## BLEU
-    bleu1 = caculate_bleu(rationale_data, results_reference, gram=1)
-    bleu4 = caculate_bleu(rationale_data, results_reference, gram=4)
-
-    ## Rouge-L
-    rouge = caculate_rouge(rationale_data, results_reference)
-
-    ## Similarity
-    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2').cuda()
-    similariry = caculate_similariry(rationale_data, results_reference, model)
-
-    scores = {
-            "answer":{
-                'acc_natural':
-                get_acc_with_contion(res_pd, 'subject', 'natural science'),
-                'acc_social':
-                get_acc_with_contion(res_pd, 'subject', 'social science'),
-                'acc_language':
-                get_acc_with_contion(res_pd, 'subject', 'language science'),
-                'acc_has_text':
-                get_acc_with_contion(res_pd, 'has_text', True),
-                'acc_has_image':
-                get_acc_with_contion(res_pd, 'has_image', True),
-                'acc_no_context':
-                get_acc_with_contion(res_pd, 'no_context', True),
-                'acc_grade_1_6':
-                get_acc_with_contion(res_pd, 'grade', ['grade1', 'grade2', 'grade3', 'grade4', 'grade5', 'grade6']),
-                'acc_grade_7_12':
-                get_acc_with_contion(res_pd, 'grade', ['grade7', 'grade8', 'grade9', 'grade10', 'grade11', 'grade12']),
-                'acc_average':
-                "{:.2f}".format(acc_average),
-            },
-            "rationale":{
-                'bleu1': bleu1 * 100,
-                'bleu4': bleu4 * 100,
-                'rouge': rouge * 100,
-                'similariry': similariry * 100,
-            }
-    }
-
-    return scores
-
-
-def print_scores(scores):
-    latex_output = ""
-    for key, score in scores.items():
-        print(f"{key[4:]}: \t{score}")
-        latex_output += f"& {score} "
-    latex_output += "\\\\"
-    print(latex_output)

mm-cot/mm-cot/utils_prompt.py

@@ -1,240 +0,0 @@
-'''
-Adapted from https://github.com/lupantech/ScienceQA
-'''
-
-from dataclasses import dataclass
-from typing import List, Optional
-
-def get_question_text(problem):
-    question = problem['question']
-    return question
-
-
-def get_context_text(problem, use_caption):
-    txt_context = problem['hint']
-    img_context = problem['caption'] if use_caption else ""
-    context = " ".join([txt_context, img_context]).strip()
-    if context == "":
-        context = "N/A"
-    return context
-
-
-def get_choice_text(probelm, options):
-    choices = probelm['choices']
-    choice_list = []
-    for i, c in enumerate(choices):
-        choice_list.append("({}) {}".format(options[i], c))
-    choice_txt = " ".join(choice_list)
-    #print(choice_txt)
-    return choice_txt
-
-def get_origin_answer(problem, options):
-    return problem['choices'][problem['answer']]
-
-def get_answer(problem, options):
-    return options[problem['answer']]
-
-
-def get_lecture_text(problem):
-    # \\n: GPT-3 can generate the lecture with more tokens.
-    lecture = problem['lecture'].replace("\n", "\\n")
-    return lecture
-
-
-def get_solution_text(problem):
-    # \\n: GPT-3 can generate the solution with more tokens
-    solution = problem['solution'].replace("\n", "\\n")
-    return solution
-
-
-def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True, WithOutput = False, curr_le_data=None):
-
-    input_format, output_format = format.split("-")
-
-    ## Inputs
-    if input_format == "CQM":
-        input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
-    elif input_format == "QCM":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
-    elif input_format == "QM":
-        input = f"Question: {question}\nOptions: {choice}\n"
-    elif input_format == "QC":
-        input = f"Question: {question}\nContext: {context}\n"
-    elif input_format == "QCMG":
-        if curr_le_data is not None:
-            input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n{curr_le_data}\n"
-        else:
-            input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nSolution: {lecture} {solution}\n"
-    elif input_format == "CQMG":
-        if curr_le_data is not None:
-            input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n{curr_le_data}\n"
-        else:
-            input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\nSolution: {lecture} {solution}\n"
-    # upper bound experiment
-    elif input_format == "QCML":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
-    elif input_format == "QCME":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
-    elif input_format == "QCMLE":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
-
-    elif input_format == "QCLM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
-    elif input_format == "QCEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
-    elif input_format == "QCLEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
-    elif input_format == "QCMA":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nAnswer: The answer is {answer}.\n"
-    elif input_format == "QCA":
-        input = f"Question: {question}\nContext: {context}\nAnswer: The answer is {answer}. \nBECAUSE:"
-
-    # Outputs
-    if test_example:
-        if output_format == 'A':
-            output = "Answer:"
-        elif output_format == 'E':
-            output = "Solution:"
-        else:
-            output = "Solution:"
-    elif output_format == 'A':
-        output = f"Answer: The answer is {answer}."
-
-    elif output_format == 'AL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
-    elif output_format == 'AE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
-    elif output_format == 'ALE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
-    elif output_format == 'AEL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
-
-    elif output_format == 'LA':
-        output = f"Answer: {lecture} The answer is {answer}."
-    elif output_format == 'EA':
-        output = f"Answer: {solution} The answer is {answer}."
-    elif output_format == 'LEA':
-        output = f"Answer: {lecture} {solution} The answer is {answer}."
-    elif output_format == 'ELA':
-        output = f"Answer: {solution} {lecture} The answer is {answer}."
-
-    elif output_format == 'LE':
-        output = f"Solution: {lecture} {solution}."
-
-    elif output_format == 'E':
-        output = f"Solution: {solution}"
-        
-    
-    if WithOutput:
-        if output.endswith("BECAUSE:"):
-            output = output.replace("BECAUSE:", "").strip()
-        if output_format == 'E':
-            text = input + f'Solution:'
-        elif output_format == 'A':
-            text = input + f'Answer:'
-        else: 
-            text = input + f'Solution:'
-        text = text.replace("  ", " ").strip()
-        output = output.replace("  ", " ").strip()
-        return text, output
-        
-        
-    text = input + output
-    text = text.replace("  ", " ").strip()
-    if text.endswith("BECAUSE:"):
-        text = text.replace("BECAUSE:", "").strip()
-    return text
-
-
-def build_prompt(problems, shot_qids, test_qid, args):
-
-    examples = []
-
-    # n-shot training examples
-    for qid in shot_qids:
-        question = get_question_text(problems[qid])
-        context = get_context_text(problems[qid], args.use_caption)
-        choice = get_choice_text(problems[qid], args.options)
-        answer = get_answer(problems[qid], args.options)
-        lecture = get_lecture_text(problems[qid])
-        solution = get_solution_text(problems[qid])
-
-        train_example = create_one_example(args.prompt_format,
-                                           question,
-                                           context,
-                                           choice,
-                                           answer,
-                                           lecture,
-                                           solution,
-                                           test_example=False)
-        examples.append(train_example)
-
-    # test example
-    question = get_question_text(problems[test_qid])
-    context = get_context_text(problems[test_qid], args.use_caption)
-    choice = get_choice_text(problems[test_qid], args.options)
-    answer = get_answer(problems[test_qid], args.options)
-    lecture = get_lecture_text(problems[test_qid])
-    solution = get_solution_text(problems[test_qid])
-
-    test_example = create_one_example(args.prompt_format,
-                                      question,
-                                      context,
-                                      choice,
-                                      answer,
-                                      lecture,
-                                      solution,
-                                      test_example=True)
-    examples.append(test_example)
-
-    # create the prompt input
-    prompt_input = '\n\n'.join(examples)
-
-    return prompt_input
-
-def build_train_pair(problems, test_qid, args, curr_le_data=None):
-
-    examples = []
-
-    # test example
-    question = get_question_text(problems[test_qid])
-    context = get_context_text(problems[test_qid], args.use_caption)
-    choice = get_choice_text(problems[test_qid], args.options)
-    
-    lecture = get_lecture_text(problems[test_qid])
-    solution = get_solution_text(problems[test_qid])
-
-    # answer_text = get_origin_answer(problems[test_qid], args.options)
-    answer_option = get_answer(problems[test_qid], args.options)
-    answer = "(" + answer_option + ")"
-    
-    test_example, target = create_one_example(args.prompt_format,
-                                      question,
-                                      context,
-                                      choice,
-                                      answer,
-                                      lecture,
-                                      solution,
-                                      test_example=False,WithOutput = True, curr_le_data=curr_le_data)
-    examples.append(test_example)
-    
-    target = target.replace("Answer:", "").strip()
-    # create the prompt input
-    prompt_input = '\n\n'.join(examples)
-
-    return prompt_input, target
-
-@dataclass(frozen=True)
-class InputFeatures:
-    """
-    A single set of features of data.
-    Property names are the same names as the corresponding inputs to a model.
-    """
-
-    input_ids: List[List[int]]
-    attention_mask: Optional[List[List[int]]]
-    token_type_ids: Optional[List[List[int]]]
-    le_input_ids: List[List[int]]
-    le_attention_mask: Optional[List[List[int]]]
-    le_token_type_ids: Optional[List[List[int]]]
-    label: Optional[int]

mm-cot/model.py

@@ -1,194 +0,0 @@
-'''
-Adapted from https://github.com/huggingface/transformers
-'''
-
-from transformers import T5Config, T5ForConditionalGeneration
-from transformers.models.t5.modeling_t5 import T5Stack, __HEAD_MASK_WARNING_MSG, T5EncoderModel
-import copy
-import math
-import os
-import warnings
-from typing import Optional, Tuple, Union
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers.modeling_outputs import (
-    BaseModelOutput,
-    Seq2SeqLMOutput,
-)
-
-class T5ForMultimodalGeneration(T5ForConditionalGeneration):
-    _keys_to_ignore_on_load_missing = [
-        r"encoder.embed_tokens.weight",
-        r"decoder.embed_tokens.weight",
-        r"lm_head.weight",
-    ]
-    _keys_to_ignore_on_load_unexpected = [
-        r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
-    ]
-
-    def __init__(self, config: T5Config, patch_size, padding_idx, save_dir):
-        super().__init__(config)
-        self.model_dim = config.d_model
-        
-        self.padding_idx = padding_idx
-        self.out = open(os.path.join(save_dir, 'gate.txt'), 'w')
-
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-        self.patch_num, self.patch_dim = patch_size
-
-        self.image_dense = nn.Linear(self.patch_dim, config.d_model)
-        self.mha_layer = torch.nn.MultiheadAttention(embed_dim=config.hidden_size, kdim=config.hidden_size, vdim=config.hidden_size, num_heads=1, batch_first=True)
-        self.gate_dense = nn.Linear(2*config.hidden_size, config.hidden_size)
-        self.sigmoid = nn.Sigmoid()
-
-        encoder_config = copy.deepcopy(config)
-        encoder_config.is_decoder = False
-        encoder_config.use_cache = False
-        encoder_config.is_encoder_decoder = False
-        self.encoder = T5Stack(encoder_config, self.shared)
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        decoder_config.is_encoder_decoder = False
-        decoder_config.num_layers = config.num_decoder_layers
-        self.decoder = T5Stack(decoder_config, self.shared)
-
-        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        # Model parallel
-        self.model_parallel = False
-        self.device_map = None
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        image_ids=None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        decoder_head_mask: Optional[torch.FloatTensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
-        if head_mask is not None and decoder_head_mask is None:
-            if self.config.num_layers == self.config.num_decoder_layers:
-                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
-                decoder_head_mask = head_mask
-
-        # Encode if needed (training, first prediction pass)
-        if encoder_outputs is None:
-            # Convert encoder inputs in embeddings if needed
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                inputs_embeds=inputs_embeds,
-                head_mask=head_mask,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-
-        hidden_states = encoder_outputs[0]
-        
-        image_embedding = self.image_dense(image_ids)
-        image_att, _ = self.mha_layer(hidden_states, image_embedding, image_embedding)
-
-        merge = torch.cat([hidden_states, image_att], dim=-1)
-        gate = self.sigmoid(self.gate_dense(merge))
-        hidden_states = (1 - gate) * hidden_states + gate * image_att
-
-        if self.model_parallel:
-            torch.cuda.set_device(self.decoder.first_device)
-
-        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
-            # get decoder inputs from shifting lm labels to the right
-            decoder_input_ids = self._shift_right(labels)
-
-        # Set device for model parallelism
-        if self.model_parallel:
-            torch.cuda.set_device(self.decoder.first_device)
-            hidden_states = hidden_states.to(self.decoder.first_device)
-            if decoder_input_ids is not None:
-                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
-            if attention_mask is not None:
-                attention_mask = attention_mask.to(self.decoder.first_device)
-            if decoder_attention_mask is not None:
-                decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
-
-        # Decode
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            inputs_embeds=decoder_inputs_embeds,
-            past_key_values=past_key_values,
-            encoder_hidden_states=hidden_states,
-            encoder_attention_mask=attention_mask,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = decoder_outputs[0]
-
-        # Set device for model parallelism
-        if self.model_parallel:
-            torch.cuda.set_device(self.encoder.first_device)
-            self.lm_head = self.lm_head.to(self.encoder.first_device)
-            sequence_output = sequence_output.to(self.lm_head.weight.device)
-
-        if self.config.tie_word_embeddings:
-            # Rescale output before projecting on vocab
-            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
-            sequence_output = sequence_output * (self.model_dim**-0.5)
-
-        lm_logits = self.lm_head(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss(ignore_index=-100)
-            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
-            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
-
-        if not return_dict:
-            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
-            return ((loss,) + output) if loss is not None else output
-
-        return Seq2SeqLMOutput(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )

mm-cot/requirements.txt

@@ -1,11 +0,0 @@
-huggingface-hub==0.0.12
-numpy==1.23.2
-openai==0.23.0
-pandas==1.4.3
-rouge==1.0.1
-sentence-transformers==2.2.2
-transformers==4.21.1
-nltk==3.6.6
-evaluate==0.4.0
-rouge==1.0.1
-rouge_score==0.1.2

mm-cot/run_inference.sh

@@ -1,17 +0,0 @@
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE \
-    --evaluate_dir models/rationale
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le models/rationale/predictions_ans_eval.json \
-    --test_le models/rationale/predictions_ans_test.json \
-    --evaluate_dir models/answer

mm-cot/run_training.sh

@@ -1,15 +0,0 @@
-# rationale generation
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg rationale --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
-    --final_eval --prompt_format QCM-LE
-
-# answer inference
-CUDA_VISIBLE_DEVICES=0,1 python main.py \
-    --model allenai/unifiedqa-t5-base \
-    --user_msg answer --img_type detr \
-    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
-    --final_eval --prompt_format QCMG-A \
-    --eval_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_eval.json \
-    --test_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_test.json

mm-cot/utils_data.py

@@ -1,228 +0,0 @@
-import os
-from torch.utils.data import Dataset
-import os
-import json
-import numpy as np
-import torch
-from utils_prompt import *
-
-img_shape = {
-    "resnet": (512, 2048),
-    "clip": (49, 2048),
-    "detr": (100, 256),
-}
-
-def load_data_std(args):
-    problems = json.load(open(os.path.join(args.data_root, 'scienceqa/problems.json')))
-    pid_splits = json.load(open(os.path.join(args.data_root, 'scienceqa/pid_splits.json')))
-    captions = json.load(open(args.caption_file))["captions"]
-
-    for qid in problems:
-        problems[qid]['caption'] = captions[qid] if qid in captions else ""
-
-    train_qids = pid_splits['%s' % (args.train_split)]
-    val_qids = pid_splits['%s' % (args.val_split)]
-    test_qids = pid_splits['%s' % (args.test_split)]
-    print(f"number of train problems: {len(train_qids)}\n")
-    print(f"number of val problems: {len(val_qids)}\n")
-    print(f"number of test problems: {len(test_qids)}\n")
-
-    qids = {'train': train_qids, 'val':val_qids,'test':test_qids}
-    return problems, qids,
-
-def load_data_img(args):
-    problems = json.load(open(os.path.join(args.data_root, 'scienceqa/problems.json')))
-    pid_splits = json.load(open(os.path.join(args.data_root, 'scienceqa/pid_splits.json')))
-    captions = json.load(open(args.caption_file))["captions"]
-    name_maps = json.load(open('vision_features/name_map.json'))
-
-    # check
-    if args.img_type == "resnet":
-        image_features = np.load('vision_features/resnet.npy')
-        image_features = np.expand_dims(image_features, axis=1)
-        image_features = image_features.repeat(512, axis=1)
-    elif args.img_type == "clip":
-        image_features = np.load('vision_features/clip.npy')
-    elif args.img_type == "detr":
-        image_features = np.load('vision_features/detr.npy')
-    else:
-        image_features = np.load('vision_features/detr.npy')
-    print("img_features size: ", image_features.shape)
-
-    for qid in problems:
-        problems[qid]['caption'] = captions[qid] if qid in captions else ""
-
-    train_qids = pid_splits['%s' % (args.train_split)]
-    val_qids = pid_splits['%s' % (args.val_split)]
-    test_qids = pid_splits['%s' % (args.test_split)]
-    print(f"number of train problems: {len(train_qids)}\n")
-    print(f"number of val problems: {len(val_qids)}\n")
-    print(f"number of test problems: {len(test_qids)}\n")
-
-    qids = {'train': train_qids, 'val':val_qids,'test':test_qids}
-    return problems, qids, name_maps, image_features
-
-class ScienceQADatasetStd(Dataset):
-    """
-    Creating a custom dataset for reading the dataset and
-    loading it into the dataloader to pass it to the
-    neural network for finetuning the model
-
-    """
-
-    def __init__(
-        self, problems, qids, tokenizer, source_len, target_len, args, test_le=None
-    ):
-        self.tokenizer = tokenizer
-        self.data = {qid : problems[qid] for qid in qids}
-        self.source_len = source_len
-        self.summ_len = target_len
-        self.target_text = []
-        self.source_text = []
-        if test_le is not None:
-            test_le_data =json.load(open(test_le))["preds"]
-        else:
-            test_le_data = None
-        idx = 0
-        for qid in self.data:
-            if test_le_data is not None:
-                curr_le_data = test_le_data[idx]
-                idx += 1
-            else:
-                curr_le_data = None
-            prompt, target = build_train_pair(problems, qid, args, curr_le_data)
-            self.target_text.append(target)
-            self.source_text.append(prompt)
-
-    def __len__(self):
-        return len(self.target_text)
-
-    def __getitem__(self, index):
-        source_text = str(self.source_text[index])
-        target_text = str(self.target_text[index])
-
-        # cleaning data so as to ensure data is in string type
-        source_text = " ".join(source_text.split())
-        target_text = " ".join(target_text.split())
-
-        source = self.tokenizer.batch_encode_plus(
-            [source_text],
-            max_length=self.source_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        target = self.tokenizer.batch_encode_plus(
-            [target_text],
-            max_length=self.summ_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        source_ids = source["input_ids"].squeeze()
-        source_mask = source["attention_mask"].squeeze()
-        target_ids = target["input_ids"].squeeze().tolist()
-        
-        return {
-            "input_ids": source_ids,
-            "attention_mask": source_mask,
-            "labels": target_ids,
-        }
-
-
-class ScienceQADatasetImg(Dataset):
-    """
-    Creating a custom dataset for reading the dataset and
-    loading it into the dataloader to pass it to the
-    neural network for finetuning the model
-
-    """
-
-    def __init__(
-        self, problems, qids, name_maps, tokenizer, source_len, target_len, args, image_features, test_le=None
-    ):
-        """
-        Initializes a Dataset class
-
-        Args:
-            dataframe (pandas.DataFrame): Input dataframe
-            tokenizer (transformers.tokenizer): Transformers tokenizer
-            source_len (int): Max length of source text
-            target_len (int): Max length of target text
-            source_text (str): column name of source text
-            target_text (str): column name of target text
-        """
-        self.tokenizer = tokenizer
-        self.data = {qid : problems[qid] for qid in qids}
-        self.source_len = source_len
-        self.summ_len = target_len
-        self.target_text = []
-        self.source_text = []
-        self.image_ids = []
-        if test_le is not None:
-            test_le_data =json.load(open(test_le))["preds"]
-        else:
-            test_le_data = None
-        idx = 0
-        for qid in self.data:
-            if test_le_data is not None:
-                curr_le_data = test_le_data[idx]
-                idx += 1
-            else:
-                curr_le_data = None
-            prompt, target = build_train_pair(problems, qid, args, curr_le_data)
-            self.target_text.append(target)
-            self.source_text.append(prompt)
-            if str(qid) in name_maps:
-                i_vectors = image_features[int(name_maps[str(qid)])]
-                self.image_ids.append(i_vectors)
-            else:
-                shape = img_shape[args.img_type]
-                self.image_ids.append(np.zeros(shape))
-    
-    def __len__(self):
-        """returns the length of dataframe"""
-
-        return len(self.target_text)
-
-    def __getitem__(self, index):
-        """return the input ids, attention masks and target ids"""
-
-        source_text = str(self.source_text[index])
-        target_text = str(self.target_text[index])
-        image_ids = self.image_ids[index]
-
-        # cleaning data so as to ensure data is in string type
-        source_text = " ".join(source_text.split())
-        target_text = " ".join(target_text.split())
-
-        source = self.tokenizer.batch_encode_plus(
-            [source_text],
-            max_length=self.source_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        target = self.tokenizer.batch_encode_plus(
-            [target_text],
-            max_length=self.summ_len,
-            pad_to_max_length=True,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        source_ids = source["input_ids"].squeeze()
-        source_mask = source["attention_mask"].squeeze()
-        target_ids = target["input_ids"].squeeze().tolist()
-
-        image_ids = torch.tensor(image_ids).squeeze()
-        
-        return {
-            "input_ids": source_ids,
-            "attention_mask": source_mask,
-            "image_ids": image_ids,
-            "labels": target_ids,
-        }

mm-cot/utils_evaluate.py

@@ -1,108 +0,0 @@
-'''
-Adapted from https://github.com/lupantech/ScienceQA
-'''
-
-import os
-import json
-import argparse
-import warnings
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-from evaluations import caculate_bleu, caculate_rouge, caculate_similariry
-
-warnings.filterwarnings('ignore')
-
-def get_acc_with_contion(res_pd, key, values):
-    if isinstance(values, list):
-        total_pd = res_pd[res_pd[key].isin(values)]
-    else:
-        total_pd = res_pd[res_pd[key] == values]
-    correct_pd = total_pd[total_pd['true_false'] == True]
-    acc = "{:.2f}".format(len(correct_pd) / len(total_pd) * 100)
-    return acc
-
-
-def get_scores(result_data, rationale_data, results_reference, data_file):
-    # read result file
-    results = result_data
-    num = len(results)
-    assert num == 4241
-    #print("number of questions:", num)
-
-    # read data file
-    sqa_data = json.load(open(data_file))
-
-    # construct pandas data
-    sqa_pd = pd.DataFrame(sqa_data).T
-    res_pd = sqa_pd[sqa_pd['split'] == 'test']  # test set
-
-    # update data
-    for index, row in res_pd.iterrows():
-
-        res_pd.loc[index, 'no_context'] = True if (not row['hint'] and not row['image']) else False
-        res_pd.loc[index, 'has_text'] = True if row['hint'] else False
-        res_pd.loc[index, 'has_image'] = True if row['image'] else False
-        res_pd.loc[index, 'has_text_image'] = True if (row['hint'] and row['image']) else False
-
-        label = row['answer']
-        pred = int(results[index])
-        res_pd.loc[index, 'pred'] = pred
-        res_pd.loc[index, 'true_false'] = (label == pred)
-
-    # accuracy scores
-    acc_average = len(res_pd[res_pd['true_false'] == True]) / num * 100
-    #assert result_file.split('_')[-1] == "{:.3f}.json".format(acc_average)
-
-
-    # rationale quality
-
-    ## BLEU
-    bleu1 = caculate_bleu(rationale_data, results_reference, gram=1)
-    bleu4 = caculate_bleu(rationale_data, results_reference, gram=4)
-
-    ## Rouge-L
-    rouge = caculate_rouge(rationale_data, results_reference)
-
-    ## Similarity
-    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2').cuda()
-    similariry = caculate_similariry(rationale_data, results_reference, model)
-
-    scores = {
-            "answer":{
-                'acc_natural':
-                get_acc_with_contion(res_pd, 'subject', 'natural science'),
-                'acc_social':
-                get_acc_with_contion(res_pd, 'subject', 'social science'),
-                'acc_language':
-                get_acc_with_contion(res_pd, 'subject', 'language science'),
-                'acc_has_text':
-                get_acc_with_contion(res_pd, 'has_text', True),
-                'acc_has_image':
-                get_acc_with_contion(res_pd, 'has_image', True),
-                'acc_no_context':
-                get_acc_with_contion(res_pd, 'no_context', True),
-                'acc_grade_1_6':
-                get_acc_with_contion(res_pd, 'grade', ['grade1', 'grade2', 'grade3', 'grade4', 'grade5', 'grade6']),
-                'acc_grade_7_12':
-                get_acc_with_contion(res_pd, 'grade', ['grade7', 'grade8', 'grade9', 'grade10', 'grade11', 'grade12']),
-                'acc_average':
-                "{:.2f}".format(acc_average),
-            },
-            "rationale":{
-                'bleu1': bleu1 * 100,
-                'bleu4': bleu4 * 100,
-                'rouge': rouge * 100,
-                'similariry': similariry * 100,
-            }
-    }
-
-    return scores
-
-
-def print_scores(scores):
-    latex_output = ""
-    for key, score in scores.items():
-        print(f"{key[4:]}: \t{score}")
-        latex_output += f"& {score} "
-    latex_output += "\\\\"
-    print(latex_output)

mm-cot/utils_prompt.py

@@ -1,240 +0,0 @@
-'''
-Adapted from https://github.com/lupantech/ScienceQA
-'''
-
-from dataclasses import dataclass
-from typing import List, Optional
-
-def get_question_text(problem):
-    question = problem['question']
-    return question
-
-
-def get_context_text(problem, use_caption):
-    txt_context = problem['hint']
-    img_context = problem['caption'] if use_caption else ""
-    context = " ".join([txt_context, img_context]).strip()
-    if context == "":
-        context = "N/A"
-    return context
-
-
-def get_choice_text(probelm, options):
-    choices = probelm['choices']
-    choice_list = []
-    for i, c in enumerate(choices):
-        choice_list.append("({}) {}".format(options[i], c))
-    choice_txt = " ".join(choice_list)
-    #print(choice_txt)
-    return choice_txt
-
-def get_origin_answer(problem, options):
-    return problem['choices'][problem['answer']]
-
-def get_answer(problem, options):
-    return options[problem['answer']]
-
-
-def get_lecture_text(problem):
-    # \\n: GPT-3 can generate the lecture with more tokens.
-    lecture = problem['lecture'].replace("\n", "\\n")
-    return lecture
-
-
-def get_solution_text(problem):
-    # \\n: GPT-3 can generate the solution with more tokens
-    solution = problem['solution'].replace("\n", "\\n")
-    return solution
-
-
-def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True, WithOutput = False, curr_le_data=None):
-
-    input_format, output_format = format.split("-")
-
-    ## Inputs
-    if input_format == "CQM":
-        input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
-    elif input_format == "QCM":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
-    elif input_format == "QM":
-        input = f"Question: {question}\nOptions: {choice}\n"
-    elif input_format == "QC":
-        input = f"Question: {question}\nContext: {context}\n"
-    elif input_format == "QCMG":
-        if curr_le_data is not None:
-            input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n{curr_le_data}\n"
-        else:
-            input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nSolution: {lecture} {solution}\n"
-    elif input_format == "CQMG":
-        if curr_le_data is not None:
-            input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n{curr_le_data}\n"
-        else:
-            input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\nSolution: {lecture} {solution}\n"
-    # upper bound experiment
-    elif input_format == "QCML":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
-    elif input_format == "QCME":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
-    elif input_format == "QCMLE":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
-
-    elif input_format == "QCLM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
-    elif input_format == "QCEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
-    elif input_format == "QCLEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
-    elif input_format == "QCMA":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nAnswer: The answer is {answer}.\n"
-    elif input_format == "QCA":
-        input = f"Question: {question}\nContext: {context}\nAnswer: The answer is {answer}. \nBECAUSE:"
-
-    # Outputs
-    if test_example:
-        if output_format == 'A':
-            output = "Answer:"
-        elif output_format == 'E':
-            output = "Solution:"
-        else:
-            output = "Solution:"
-    elif output_format == 'A':
-        output = f"Answer: The answer is {answer}."
-
-    elif output_format == 'AL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
-    elif output_format == 'AE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
-    elif output_format == 'ALE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
-    elif output_format == 'AEL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
-
-    elif output_format == 'LA':
-        output = f"Answer: {lecture} The answer is {answer}."
-    elif output_format == 'EA':
-        output = f"Answer: {solution} The answer is {answer}."
-    elif output_format == 'LEA':
-        output = f"Answer: {lecture} {solution} The answer is {answer}."
-    elif output_format == 'ELA':
-        output = f"Answer: {solution} {lecture} The answer is {answer}."
-
-    elif output_format == 'LE':
-        output = f"Solution: {lecture} {solution}."
-
-    elif output_format == 'E':
-        output = f"Solution: {solution}"
-        
-    
-    if WithOutput:
-        if output.endswith("BECAUSE:"):
-            output = output.replace("BECAUSE:", "").strip()
-        if output_format == 'E':
-            text = input + f'Solution:'
-        elif output_format == 'A':
-            text = input + f'Answer:'
-        else: 
-            text = input + f'Solution:'
-        text = text.replace("  ", " ").strip()
-        output = output.replace("  ", " ").strip()
-        return text, output
-        
-        
-    text = input + output
-    text = text.replace("  ", " ").strip()
-    if text.endswith("BECAUSE:"):
-        text = text.replace("BECAUSE:", "").strip()
-    return text
-
-
-def build_prompt(problems, shot_qids, test_qid, args):
-
-    examples = []
-
-    # n-shot training examples
-    for qid in shot_qids:
-        question = get_question_text(problems[qid])
-        context = get_context_text(problems[qid], args.use_caption)
-        choice = get_choice_text(problems[qid], args.options)
-        answer = get_answer(problems[qid], args.options)
-        lecture = get_lecture_text(problems[qid])
-        solution = get_solution_text(problems[qid])
-
-        train_example = create_one_example(args.prompt_format,
-                                           question,
-                                           context,
-                                           choice,
-                                           answer,
-                                           lecture,
-                                           solution,
-                                           test_example=False)
-        examples.append(train_example)
-
-    # test example
-    question = get_question_text(problems[test_qid])
-    context = get_context_text(problems[test_qid], args.use_caption)
-    choice = get_choice_text(problems[test_qid], args.options)
-    answer = get_answer(problems[test_qid], args.options)
-    lecture = get_lecture_text(problems[test_qid])
-    solution = get_solution_text(problems[test_qid])
-
-    test_example = create_one_example(args.prompt_format,
-                                      question,
-                                      context,
-                                      choice,
-                                      answer,
-                                      lecture,
-                                      solution,
-                                      test_example=True)
-    examples.append(test_example)
-
-    # create the prompt input
-    prompt_input = '\n\n'.join(examples)
-
-    return prompt_input
-
-def build_train_pair(problems, test_qid, args, curr_le_data=None):
-
-    examples = []
-
-    # test example
-    question = get_question_text(problems[test_qid])
-    context = get_context_text(problems[test_qid], args.use_caption)
-    choice = get_choice_text(problems[test_qid], args.options)
-    
-    lecture = get_lecture_text(problems[test_qid])
-    solution = get_solution_text(problems[test_qid])
-
-    # answer_text = get_origin_answer(problems[test_qid], args.options)
-    answer_option = get_answer(problems[test_qid], args.options)
-    answer = "(" + answer_option + ")"
-    
-    test_example, target = create_one_example(args.prompt_format,
-                                      question,
-                                      context,
-                                      choice,
-                                      answer,
-                                      lecture,
-                                      solution,
-                                      test_example=False,WithOutput = True, curr_le_data=curr_le_data)
-    examples.append(test_example)
-    
-    target = target.replace("Answer:", "").strip()
-    # create the prompt input
-    prompt_input = '\n\n'.join(examples)
-
-    return prompt_input, target
-
-@dataclass(frozen=True)
-class InputFeatures:
-    """
-    A single set of features of data.
-    Property names are the same names as the corresponding inputs to a model.
-    """
-
-    input_ids: List[List[int]]
-    attention_mask: Optional[List[List[int]]]
-    token_type_ids: Optional[List[List[int]]]
-    le_input_ids: List[List[int]]
-    le_attention_mask: Optional[List[List[int]]]
-    le_token_type_ids: Optional[List[List[int]]]
-    label: Optional[int]