[Feature] Support VindLU multi-modality algorithm (#2667)

open-mmlab · Sep 7, 2023 · baf385e · baf385e
1 parent dd1901a
commit baf385e
Show file tree

Hide file tree

Showing 41 changed files with 5,768 additions and 21 deletions.
diff --git a/.circleci/test.yml b/.circleci/test.yml
@@ -73,6 +73,10 @@ jobs:
           name: Install timm
           command: |
             pip install timm
+      - run:
+          name: Install transformers
+          command: |
+            pip install transformers
       - when:
           condition:
             equal: [ "0.10.0", << parameters.torchvision >> ]
@@ -118,6 +122,10 @@ jobs:
           command: |
             docker exec mmaction pip install timm
             docker exec mmaction python -m pip install pytorchvideo
+      - run:
+          name: Install transformers
+          command: |
+            docker exec mmaction pip install transformers
       - run:
           name: Install mmaction dependencies
           command: |

diff --git a/.github/workflows/merge_stage_test.yml b/.github/workflows/merge_stage_test.yml
@@ -69,6 +69,8 @@ jobs:
         if: ${{matrix.torchvision == '0.10.0'}}
       - name: Install timm
         run: pip install timm
+      - name: Install transformers
+        run: pip install transformers
       - name: Build and install
         run: rm -rf .eggs && pip install -e .
       - name: Run unittests and generate coverage report
@@ -110,6 +112,8 @@ jobs:
         run: pip install lmdb
       - name: Install timm
         run: pip install timm
+      - name: Install transformers
+        run: pip install transformers
       - name: Install TurboJpeg lib
         run: sudo apt-get install -y libturbojpeg
       - name: Install PyTorch
@@ -183,6 +187,8 @@ jobs:
         run: pip install librosa soundfile
       - name: Install lmdb
         run: pip install lmdb
+      - name: Install transformers
+        run: pip install transformers
       - name: Install mmaction dependencies
         run: |
           pip install git+https://github.com/open-mmlab/mmengine.git@main
@@ -240,6 +246,8 @@ jobs:
         run: python -m pip install pytorchvideo
       - name: Install timm
         run: python -m pip install timm
+      - name: Install transformers
+        run: python -m pip install transformers
       - name: Build and install
         run: |
           pip install -e . -v

diff --git a/configs/multimodal/vindlu/README.md b/configs/multimodal/vindlu/README.md
@@ -0,0 +1,87 @@
+# VindLU
+
+[VindLU: A Recipe for Effective Video-and-Language Pretraining](https://arxiv.org/abs/2212.05051)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+The last several years have witnessed remarkable progress in video-and-language (VidL) understanding. However, most modern VidL approaches use complex and specialized model architectures and sophisticated pretraining protocols, making the reproducibility, analysis and comparisons of these frameworks difficult. Hence, instead of proposing yet another new VidL model, this paper conducts a thorough empirical study demystifying the most important factors in the VidL model design. Among the factors that we investigate are (i) the spatiotemporal architecture design, (ii) the multimodal fusion schemes, (iii) the pretraining objectives, (iv) the choice of pretraining data, (v) pretraining and finetuning protocols, and (vi) dataset and model scaling. Our empirical study reveals that the most important design factors include: temporal modeling, video-to-text multimodal fusion, masked modeling objectives, and joint training on images and videos. Using these empirical insights, we then develop a step-by-step recipe, dubbed VindLU, for effective VidL pretraining. Our final model trained using our recipe achieves comparable or better than state-of-the-art results on several VidL tasks without relying on external CLIP pretraining. In particular, on the text-to-video retrieval task, our approach obtains 61.2% on DiDeMo, and 55.0% on ActivityNet, outperforming current SOTA by 7.8% and 6.1% respectively. Furthermore, our model also obtains state-of-the-art video question-answering results on ActivityNet-QA, MSRVTT-QA, MSRVTT-MC and TVQA. Our code and pretrained models are publicly available at: https://github.com/klauscc/VindLU.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://github.com/open-mmlab/mmaction2/assets/33249023/3efb02d0-679f-4ce7-b8f1-7f331905d902" width="800"/>
+</div>
+
+## Results and Models
+
+### Video Retrieval on MSRVTT-9k
+
+| frame sampling strategy | resolution | gpus | vision encoder | text encoder |      pretraining       | Recall@1 |                config                 |                ckpt                 |                 log                 |
+| :---------------------: | :--------: | :--: | :------------: | :----------: | :--------------------: | :------: | :-----------------------------------: | :---------------------------------: | :---------------------------------: |
+|       uniform 12        |  224x224   |  8   |   BEiT-Base    |  Bert-Base   | C5M (WebVid-2M + CC3M) |   44.0   | [config](/configs/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k/vindlu_beit-base_8x16_retrieval_msrvtt-9k_20230905-fc36231e.pth) | [log](https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k/vindlu_beit-base_8x16_retrieval_msrvtt-9k.log) |
+
+### Video Question-Answering on MSRVTT-QA
+
+| frame sampling strategy | resolution | gpus | vision encoder | text encoder |      pretraining       | top1 acc |                config                 |                ckpt                 |                 log                 |
+| :---------------------: | :--------: | :--: | :------------: | :----------: | :--------------------: | :------: | :-----------------------------------: | :---------------------------------: | :---------------------------------: |
+|       uniform 12        |  224x224   |  8   |   BEiT-Base    |  Bert-Base   | C5M (WebVid-2M + CC3M) |   43.6   | [config](/configs/multimodal/vindlu/vindlu_beit-base_8x8_vqa_msrvtt-qa.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x8_vqa_msrvtt-qa/vindlu_beit-base_8x8_vqa_msrvtt-qa_20230906-6e693e64.pth) | [log](https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x8_vqa_msrvtt-qa/vindlu_beit-base_8x8_vqa_msrvtt-qa.log) |
+
+### Multiple-Choice Question-Answering on MSRVTT-MC (Inference)
+
+| frame sampling strategy | resolution | gpus | vision encoder | text encoder |      pretraining       | top1 acc |                         config                         |                         ckpt                          |
+| :---------------------: | :--------: | :--: | :------------: | :----------: | :--------------------: | :------: | :----------------------------------------------------: | :---------------------------------------------------: |
+|       uniform 12        |  224x224   |  8   |   BEiT-Base    |  Bert-Base   | C5M (WebVid-2M + CC3M) |   97.6   | [config](/configs/multimodal/vindlu/vindlu_beit-base_vqa-mc_msrvtt-mc.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k/vindlu_beit-base_8x16_retrieval_msrvtt-9k_20230905-fc36231e.pth) |
+
+1. Currently, we only support the fine-tuning stage of VindLU models based on the pretrained checkpoint provided by the [original repo](https://github.com/klauscc/VindLU).
+
+For more details on data preparation, you can refer to [prepare msrvtt](/tools/data/msrvtt/README.md).
+
+## Train
+
+You can use the following command to train a model.
+
+```shell
+python tools/train.py ${CONFIG_FILE} [optional arguments]
+```
+
+Example: train VindLU model on MSRVTT-9k dataset in a deterministic option with periodic validation.
+
+```shell
+python tools/train.py configs/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k.py \
+    --seed 0 --deterministic
+```
+
+For more details, you can refer to the **Training** part in the [Training and Test Tutorial](/docs/en/user_guides/train_test.md).
+
+## Test
+
+You can use the following command to test a model.
+
+```shell
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
+```
+
+Example: test CLIP4Clip model on MSRVTT-9k dataset and dump the result to a pkl file.
+
+```shell
+python tools/test.py cconfigs/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k.py \
+    checkpoints/SOME_CHECKPOINT.pth --dump result.pkl
+```
+
+For more details, you can refer to the **Test** part in the [Training and Test Tutorial](/docs/en/user_guides/train_test.md).
+
+## Citation
+
+```BibTeX
+@inproceedings{cheng2023vindlu,
+  title={Vindlu: A recipe for effective video-and-language pretraining},
+  author={Cheng, Feng and Wang, Xizi and Lei, Jie and Crandall, David and Bansal, Mohit and Bertasius, Gedas},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={10739--10750},
+  year={2023}
+}
+```
diff --git a/configs/multimodal/vindlu/metafile.yml b/configs/multimodal/vindlu/metafile.yml
@@ -0,0 +1,55 @@
+Collections:
+  - Name: VindLU
+    README: configs/multimodal/vindlu/README.md
+    Paper:
+      URL: https://arxiv.org/abs/2212.05051
+      Title: 'VindLU: A Recipe for Effective Video-and-Language Pretraining'
+
+Models:
+  - Name: vindlu_beit-base_8x16_retrieval_msrvtt-9k
+    Config: configs/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k.py
+    In Collection: VindLU
+    Metadata:
+      Architecture: BEiT-Base
+      Batch Size: 16
+      Epochs: 5
+      Training Data: MSRVTT-9k
+      Training Resources: 8 GPUs
+    Results:
+      Dataset: MSRVTT
+      Task: Video Retrieval
+      Metrics:
+        Recall@1: 44.0
+        Recall@5: 70.6
+        Recall@10: 80.0
+    Training Log: https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k/vindlu_beit-base_8x16_retrieval_msrvtt-9k.log
+    Weights: https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k/vindlu_beit-base_8x16_retrieval_msrvtt-9k_20230905-fc36231e.pth
+
+  - Name: vindlu_beit-base_8x8_vqa_msrvtt-qa
+    Config: configs/multimodal/vindlu/vindlu_beit-base_8x8_vqa_msrvtt-qa.py
+    In Collection: VindLU
+    Metadata:
+      Architecture: BEiT-Base
+      Batch Size: 8
+      Epochs: 10
+      Training Data: MSRVTT-qa
+      Training Resources: 8 GPUs
+    Results:
+      Dataset: MSRVTT
+      Task: Video Question-Answering
+      Metrics:
+        Top 1 Accuracy: 43.6
+    Training Log: https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x8_vqa_msrvtt-qa/vindlu_beit-base_8x8_vqa_msrvtt-qa.log
+    Weights: https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x8_vqa_msrvtt-qa/vindlu_beit-base_8x8_vqa_msrvtt-qa_20230906-6e693e64.pth
+
+  - Name: vindlu_beit-base_vqa-mc_msrvtt-mc
+    Config: configs/multimodal/vindlu/vindlu_beit-base_vqa-mc_msrvtt-mc.py
+    In Collection: VindLU
+    Metadata:
+      Architecture: BEiT-Base
+    Results:
+      Dataset: MSRVTT-MC
+      Task: Multiple-Choice Question-Answering
+      Metrics:
+        Top 1 Accuracy: 97.6
+    Weights: https://download.openmmlab.com/mmaction/v1.0/multimodal/vindlu/vindlu_beit-base_8x16_retrieval_msrvtt-9k/vindlu_beit-base_8x16_retrieval_msrvtt-9k_20230905-fc36231e.pth