add vlmo

vmlo/DATA.md

+(The following contents are from [the ViLT repo](https://github.com/dandelin/ViLT/blob/master/DATA.md).)
+
+# Dataset Preparation
+We utilize seven datsets: Google Conceptual Captions (GCC), Stony Brook University Captions (SBU), Visual Genome (VG), COCO Captions (COCO), Flickr 30K Captions (F30K), Visual Question Answering v2 (VQAv2), and Natural Language for Visual Reasoning 2 (NLVR2).
+
+We do not distribute datasets because of the license issue.
+Please download the datasets by yourself.
+We use `pyarrow` to serialize the datasets, conversion scripts are located in `vilt/utils/write_*.py`.
+Please organize the datasets as follows and run `make_arrow` functions to convert the dataset to pyarrow binary file.
+
+## GCC
+https://ai.google.com/research/ConceptualCaptions/download
+
+GCC provides tuples of image url and caption, note that a quite portion of the urls are unaccessible now.
+Write your own download script and organize the dataset as following structure.
+
+    root
+    ├── images_train            
+    │   ├── 0000                # First four letters of image name
+    │   │   ├── 0000000         # Image Binary
+    │   │   ├── 0000001      
+    │   │   └── ...
+    │   ├── 0001              
+    │   │   ├── 0001000      
+    │   │   ├── 0001001      
+    │   │   └── ...          
+    │   └── ...          
+    ├── images_val          
+    │   ├── 0000              
+    │   │   └── ...
+    │   └── ...          
+    ├── train_annot.json        # List of (image_file_path, caption) tuple
+    └── val_annot.json          # List of (image_file_path, caption) tuple
+
+```python
+from vlmo.utils.write_conceptual_caption import make_arrow
+make_arrow(root, arrows_root)
+```
+
+## SBU
+http://www.cs.virginia.edu/~vicente/sbucaptions/
+
+Similar to GCC, SBU also provides tuples of image url and caption, and also a quite portion of the urls are unaccessible now.
+Write your own download script and organize the dataset as following structure.
+
+    root
+    ├── images_train            
+    │   ├── 0000                # First four letters of image name
+    │   │   ├── 0000000         # Image Binary
+    │   │   ├── 0000001      
+    │   │   └── ...
+    │   ├── 0001              
+    │   │   ├── 0001000      
+    │   │   ├── 0001001      
+    │   │   └── ...          
+    │   └── ...          
+    └── annot.json              # List of (image_file_path, caption) tuple
+
+```python
+from vlmo.utils.write_sbu import make_arrow
+make_arrow(root, arrows_root)
+```
+
+## VG
+http://visualgenome.org/api/v0/api_home.html
+
+Download [image part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [image part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip) and [region descriptions](http://visualgenome.org/static/data/dataset/region_descriptions.json.zip)
+
+    root
+    ├── images            
+    │   ├── VG_100K                  
+    │   │   ├── 10.jpg        
+    │   │   ├── 107899.jpg      
+    │   │   └── ...
+    │   ├── VG_100K_2              
+    │   │   ├── 1.jpg      
+    │   │   ├── 100.jpg      
+    │   │   └── ...          
+    │   └── ...          
+    └── annotations         
+        └── region_descriptions.json
+
+```python
+from vlmo.utils.write_vg import make_arrow
+make_arrow(root, arrows_root)
+```
+
+## COCO
+https://cocodataset.org/#download
+
+Download [2014 train images](http://images.cocodataset.org/zips/train2014.zip), [2014 val images](http://images.cocodataset.org/zips/val2014.zip) and [karpathy split](https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip)
+
+    root
+    ├── train2014            
+    │   ├── COCO_train2014_000000000009.jpg                
+    |   └── ...
+    ├── val2014              
+    |   ├── COCO_val2014_000000000042.jpg
+    |   └── ...          
+    └── karpathy
+        └── dataset_coco.json
+
+```python
+from vlmo.utils.write_coco_karpathy import make_arrow
+make_arrow(root, arrows_root)
+```
+
+## F30K
+http://bryanplummer.com/Flickr30kEntities/
+
+Sign [flickr images request form](https://forms.illinois.edu/sec/229675) and download [karpathy split](https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip)
+
+    root
+    ├── flickr30k-images            
+    │   ├── 1000092795.jpg
+    |   └── ...
+    └── karpathy
+        └── dataset_flickr30k.json
+
+```python
+from vlmo.utils.write_f30k_karpathy import make_arrow
+make_arrow(root, arrows_root)
+```
+
+## VQAv2
+https://visualqa.org/download.html
+
+Download COCO [2014 train images](http://images.cocodataset.org/zips/train2014.zip), [2014 val images](http://images.cocodataset.org/zips/val2014.zip), [2015 test images](http://images.cocodataset.org/zips/test2015.zip), annotations ([train](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Train_mscoco.zip), [val](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip)), and questions ([train](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Train_mscoco.zip), [val](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Val_mscoco.zip), [test](https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Test_mscoco.zip))
+
+    root
+    ├── train2014            
+    │   ├── COCO_train2014_000000000009.jpg                
+    |   └── ...
+    ├── val2014              
+    |   ├── COCO_val2014_000000000042.jpg
+    |   └── ...  
+    ├── test2015              
+    |   ├── COCO_test2015_000000000001.jpg
+    |   └── ...         
+    ├── v2_OpenEnded_mscoco_train2014_questions.json
+    ├── v2_OpenEnded_mscoco_val2014_questions.json
+    ├── v2_OpenEnded_mscoco_test2015_questions.json
+    ├── v2_OpenEnded_mscoco_test-dev2015_questions.json
+    ├── v2_mscoco_train2014_annotations.json
+    └── v2_mscoco_val2014_annotations.json
+
+```python
+from vlmo.utils.write_vqa import make_arrow
+make_arrow(root, arrows_root)
+```
+
+## NLVR2
+Clone the [repository](https://github.com/lil-lab/nlvr) and sign the [request form](https://goo.gl/forms/yS29stWnFWzrDBFH3) to download the images.
+
+    root
+    ├── images/train           
+    │   ├── 0                  
+    │   │   ├── train-10108-0-img0.png   
+    │   │   └── ...
+    │   ├── 1                  
+    │   │   ├── train-10056-0-img0.png       
+    │   │   └── ...
+    │   └── ...
+    ├── dev       
+    │   ├── dev-0-0-img0.png
+    |   └── ...
+    ├── test1     
+    │   ├── test1-0-0-img0.png
+    |   └── ...
+    ├── nlvr
+    ├── nlvr2
+    └── README.md
+
+```python
+from vlmo.utils.write_nlvr2 import make_arrow
+make_arrow(root, arrows_root)
+```
+
+## WikiBK (Text only data)
+```python
+from vlmo.utils.write_wikibk import make_arrow
+make_arrow(root, arrows_root)
+```

vmlo/README.md

+# VLMo - General-purpose Multimodal Pre-training
+
+Paper: [VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts](https://arxiv.org/abs/2111.02358).
+
+Official PyTorch implementation and pre-trained models of VLMo.
+
+- Dec, 2022: Code & model release. 
+- Sep, 2022: [**VLMo**](https://arxiv.org/pdf/2111.02358.pdf) was accepted by NeurIPS 2022.
+- May 30th, 2022: new version of [**VLMo** paper on arXiv](https://arxiv.org/pdf/2111.02358.pdf).
+- November 24th, 2021: **VLMo** Large (**single** model) as the new SOTA on the [VQA Challenge](https://eval.ai/web/challenges/challenge-page/830/leaderboard/2278)
+- Nov 2021: release preprint in [arXiv](https://arxiv.org/abs/2111.02358)
+
+## Pre-trained Models
+
+We provide three VLMo weights pre-trained on COCO, VG, SBU and GCC. The models were pre-trained with 224x224 resolution.
+
+- [`VLMo-base`](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=12; hidden=768; FFN factor=4x; #head=12; patch=16x16; #VL_FFN=2 (#parameters: 175M)
+- [`VLMo-base_plus`](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=24; hidden=544; FFN factor=4x; #head=16; patch=16x16; #VL_FFN=3 (#parameters: 167M)
+- [`VLMo-large`](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=24; hidden=1024; FFN factor=4x; #head=16; patch=16x16; #VL_FFN=3 (#parameters: 562M)
+
+## Setup
+
+```
+alias=`whoami | cut -d'.' -f2`; docker run -it --rm --runtime=nvidia --ipc=host --privileged -v /home/${alias}:/home/${alias} pytorch/pytorch:1.8.0-cuda11.1-cudnn8-devel bash
+```
+
+First, clone the repo and install required packages:
+```
+git clone https://github.com/microsoft/unilm.git
+cd unilm/vlmo
+
+pip install -r requirements.txt
+```
+
+## Dataset Preparation
+
+We process the pre-training and fine-tuning data to the same format as in [ViLT](DATA.md).
+
+## Pre-training
+
+Replace `<ARROW_ROOT>` as your data dir in following commands.
+
+### Step 1: Vision Pre-Training
+
+Download the pre-trained model weight from [BEiT repo](https://github.com/microsoft/unilm/tree/master/beit).
+
+### Step 2: Language Pre-Training (VLMo-Base)
+
+```bash
+# download from https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D
+export INIT_CKPT=/path/to/save/beit_base_checkpoint
+
+python run.py with data_root=<ARROW_ROOT> num_gpus=<NUM_GPUS> num_nodes=<NUM_NODES> task_textmlm_base whole_word_masking=True step200k per_gpu_batchsize=<BS_FITS_YOUR_GPU> load_path=$INIT_CKPT log_dir=<YOUR_OUTPUT_PATH>
+```
+Or you can download our pre-trained ckpts for this stage:
+- [`VLMo-base-stage2`](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_224_stage2.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)
+- [`VLMo-base_plus-stage2`](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_224_stage2.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)
+- [`VLMo-large-stage2`](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_224_stage2.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)
+
+### Step 3: Vision-Language Pre-Training (VLMo-Base)
+
+```bash
+
+export INIT_CKPT=/path/to/save/last_stage_ckpt
+
+python run.py with data_root=<ARROW_ROOT> num_gpus=<NUM_GPUS> num_nodes=<NUM_NODES> task_mlm_itm_itc_base whole_word_masking=True step200k per_gpu_batchsize=<BS_FITS_YOUR_GPU> load_path=$INIT_CKPT log_dir=<YOUR_OUTPUT_PATH>
+```
+
+## Fine-Tuning on Downstream Tasks
+
+## Commands
+
+```bash
+
+python run.py with data_root=<ARROW_ROOT> num_gpus=<NUM_GPUS> num_nodes=<NUM_NODES> "<CONFIG_NAME>" per_gpu_batchsize=<BS_FITS_YOUR_GPU> load_path="<VLMo_WEIGHT>" log_dir=<YOUR_OUTPUT_PATH>
+```
+
+To reduce GPU memory cost, use [Deepspeed](https://pytorch-lightning.readthedocs.io/en/stable/advanced/model_parallel.html#deepspeed-zero-stage-1) and [Activation Checkpoint](https://fairscale.readthedocs.io/en/stable/api/nn/checkpoint/checkpoint_activations.html).
+
+## Configs
+
+You can found "<CONFIG_NAME>" for each task as follows:
+
+### VQAv2
+| <CONFIG_NAME> | initialized checkpoint | finetuned weight | test-dev |
+|---------------|:----------------------:|:----------------:|:-----------:|
+|task_finetune_vqa_base_image480|[VLMo-base](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_480_vqa.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|76.6|
+|task_finetune_vqa_base_plus_image480|[VLMo-base_plus](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_480_vqa.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|78.5|
+|task_finetune_vqa_large_image480|[VLMo-large](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_480_vqa.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|79.9|
+
+### NLVR2
+| <CONFIG_NAME> | initialized checkpoint | finetuned weight | test-P |
+|---------------|:----------------------:|:----------------:|:-----------:|
+|task_finetune_nlvr2_base_image384|[VLMo-base](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_384_nlvr2.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|83.3|
+|task_finetune_nlvr2_base_plus_image384|[VLMo-base_plus](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_384_nlvr2.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|85.1|
+|task_finetune_nlvr2_large_image384|[VLMo-large](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_384_nlvr2.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|86.9|
+
+### COCO
+| <CONFIG_NAME> | initialized checkpoint | finetuned weight | TR@1 | IR@1 |
+|---------------|:----------------------:|:----------------:|:-----------:|:---:|
+|task_finetune_irtr_coco_base_image384|[VLMo-base](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_384_coco.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|74.8|57.2|
+|task_finetune_irtr_coco_base_plus_image384|[VLMo-base_plus](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_384_coco.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|76.3|58.6|
+|task_finetune_irtr_coco_large_image384|[VLMo-large](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_384_coco.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|78.2|60.6|
+
+### F30K
+| <CONFIG_NAME> | initialized checkpoint | finetuned weight | TR@1 | IR@1 |
+|---------------|:----------------------:|:----------------:|:-----------:|:---:|
+|task_finetune_irtr_f30k_base_image384|[VLMo-base_coco_finetuned](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_384_coco.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_patch16_384_f30k.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|92.3|79.3|
+|task_finetune_irtr_f30k_base_plus_image384|[VLMo-base_plus](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_224.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_base_plus_patch16_384_f30k.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|93.2|81.8|
+|task_finetune_irtr_f30k_large_image384|[VLMo-large_coco_finetuned](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_384_coco.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|[weight](https://conversationhub.blob.core.windows.net/beit-share-public/vlmo/vlmo_large_patch16_384_f30k.pt?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D)|95.3|84.5|
+
+## Evaluation
+
+To eval a finetuned model by appending `test_only=True` and set `load_path=` to the finetuned VLMo weight as follow:
+
+```bash
+python run.py with data_root=<ARROW_ROOT> num_gpus=<NUM_GPUS> num_nodes=1 "<CONFIG_NAME>" per_gpu_batchsize=<BS_FITS_YOUR_GPU> load_path="<Finetuned_VLMo_WEIGHT>" test_only=True
+```
+- For retrieval tasks, also set `get_recall_metric=True` in the command.
+
+## Acknowledgement
+
+This repository is built using the [ViLT](https://github.com/dandelin/ViLT) repository, [BEiT](https://github.com/microsoft/unilm/tree/master/beit) repository, [ALBEF](https://github.com/salesforce/ALBEF) and the [timm](https://github.com/rwightman/pytorch-image-models) library.
+
+## Citation
+
+If you find this repository useful, please consider citing our work:
+```
+@inproceedings{vlmo,
+      title={{VLMo}: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts},
+      author={Hangbo Bao and Wenhui Wang and Li Dong and Qiang Liu and Owais Khan Mohammed and Kriti Aggarwal and Subhojit Som and Songhao Piao and Furu Wei},
+      booktitle={Advances in Neural Information Processing Systems},
+      year={2022},
+      url={https://openreview.net/forum?id=bydKs84JEyw}
+}
+```
+
+
+## License
+This project is licensed under the license found in the LICENSE file in the root directory of this source tree.
+
+[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
+
+### Contact Information
+
+For help or issues using VLMo models, please submit a GitHub issue.

vmlo/gen_data.py

+# gcc
+# from vlmo.utils.write_conceptual_caption import make_arrow
+# root = "/parastor/home/chenych/MML/data/gcc"
+# arrows_root = "/parastor/home/chenych/MML/data/gcc/arrows_root"
+
+# nlvr2
+# from vlmo.utils.write_nlvr2 import make_arrow
+# root = "/home/data/nlvr-master/"
+# arrows_root = "/home/data/nlvr-master/images/arrows_root"
+
+# coco
+from vlmo.utils.write_coco_karpathy import make_arrow
+root = "/home/data/coco2014/"
+arrows_root = "/home/data/coco2014/"
+
+if __name__ == "__main__":
+    make_arrow(root, arrows_root)

vmlo/requirements.txt

+pytorch_lightning==1.5.5
+transformers==4.8.1
+Pillow==8.3.1
+tqdm==4.53.0
+ipdb==0.13.7
+einops==0.3.0
+pyarrow==2.0.0
+sacred==0.8.2
+pandas==1.1.5
+timm==0.4.12
+torchmetrics==0.7.3
+torch==1.8.0
+torchvision==0.9.0
+fairscale==0.4.0
+numpy
+scipy
+opencv-python
\ No newline at end of file

vmlo/run.py

+import os
+import copy
+import pytorch_lightning as pl
+
+from vlmo.config import ex
+from vlmo.modules import VLMo
+from vlmo.datamodules.multitask_datamodule import MTDataModule
+
+from pytorch_lightning.plugins import environments as pl_env
+from pytorch_lightning.utilities.distributed import rank_zero_info
+
+
+class OMPIClusterEnvironment(pl_env.ClusterEnvironment):
+    def __init__(self):
+        super().__init__()
+
+    # def creates_children(self) -> bool:
+    #     # return True if the cluster is managed (you don't launch processes yourself)
+    #     assert (
+    #         "OMPI_COMM_WORLD_LOCAL_RANK" in os.environ
+    #     )  # this cluster is managed
+    #     return True
+
+    @property
+    def creates_processes_externally(self):
+        return True
+
+    def world_size(self) -> int:
+        return int(os.environ["OMPI_COMM_WORLD_SIZE"])
+
+    def set_world_size(self, size: int):
+        pass
+
+    def global_rank(self) -> int:
+        return int(os.environ["OMPI_COMM_WORLD_RANK"])
+
+    def set_global_rank(self, rank: int):
+        pass
+
+    def local_rank(self) -> int:
+        return int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
+
+    def node_rank(self) -> int:
+        if "NODE_RANK" in os.environ:
+            return int(os.environ["NODE_RANK"])
+        else:
+            return 0
+
+    def master_address(self) -> str:
+        return os.environ["MASTER_ADDR"]
+
+    def master_port(self) -> int:
+        return int(os.environ["MASTER_PORT"])
+
+
+def get_cluster_plugin(num_gpus=1, num_nodes=1):
+    if num_nodes > 1 or (
+        num_nodes == 1 and "OMPI_COMM_WORLD_SIZE" in os.environ
+    ):
+        rank_zero_info("ClusterPlugin: using OMPI Cluster Environment")
+        return OMPIClusterEnvironment()
+    if num_gpus >= 1:
+        rank_zero_info("ClusterPlugin: using Lightning Cluster Environment")
+        return pl_env.LightningEnvironment()
+    return None
+
+
+@ex.automain
+def main(_config):
+    _config = copy.deepcopy(_config)
+    pl.seed_everything(_config["seed"])
+
+    dm = MTDataModule(_config, dist=True)
+
+    model = VLMo(_config)
+    exp_name = f'{_config["exp_name"]}'
+
+    os.makedirs(_config["log_dir"], exist_ok=True)
+    checkpoint_callback = pl.callbacks.ModelCheckpoint(
+        save_top_k=-1,
+        verbose=True,
+        monitor="val/the_metric",
+        mode="max",
+        save_last=True,
+    )
+    logger = pl.loggers.TensorBoardLogger(
+        _config["log_dir"],
+        name=f'{exp_name}_seed{_config["seed"]}_from_{_config["load_path"].split("/")[-1][:-5]}',
+    )
+
+    lr_callback = pl.callbacks.LearningRateMonitor(logging_interval="step")
+    callbacks = [checkpoint_callback, lr_callback]
+
+    num_gpus = (
+        _config["num_gpus"]
+        if isinstance(_config["num_gpus"], int)
+        else len(_config["num_gpus"])
+    )
+
+    grad_steps = _config["batch_size"] // (
+        _config["per_gpu_batchsize"] * num_gpus * _config["num_nodes"]
+    )
+
+    rank_zero_info("grad_steps: {}".format(grad_steps))
+
+    max_steps = _config["max_steps"] if _config["max_steps"] is not None else None
+
+    resume_ckpt = None
+    if _config["resume_during_training"]:
+        for index in range(100):
+            ckpt_path = os.path.join(_config["log_dir"], f'{exp_name}_seed{_config["seed"]}_from_{_config["load_path"].split("/")[-1][:-5]}', "version_{}/checkpoints/last.ckpt".format(index))
+            if os.path.exists(ckpt_path):
+                resume_ckpt = ckpt_path
+    
+    rank_zero_info("resume_ckpt: {}".format(resume_ckpt))
+
+    cluster_plugin = get_cluster_plugin(
+        _config["num_gpus"], _config["num_nodes"]
+    )
+    plugin_list = [cluster_plugin]
+    rank_zero_info("plugin_list: {}".format(plugin_list))
+
+    if _config["use_sharded_training"]:
+        rank_zero_info("Using ddp sharded")
+        distributed_strategy = "ddp_sharded"
+    else:
+        distributed_strategy = "ddp"
+
+    trainer = pl.Trainer(
+        gpus=_config["num_gpus"],
+        num_nodes=_config["num_nodes"],
+        precision=_config["precision"],
+        accelerator="gpu",
+        strategy=distributed_strategy,
+        benchmark=True,
+        deterministic=True,
+        max_epochs=_config["max_epoch"] if max_steps is None else 1000,
+        max_steps=max_steps,
+        callbacks=callbacks,
+        logger=logger,
+        # prepare_data_per_node=False,
+        replace_sampler_ddp=False,
+        accumulate_grad_batches=grad_steps,
+        log_every_n_steps=10,
+        flush_logs_every_n_steps=10,
+        resume_from_checkpoint=resume_ckpt,
+        weights_summary="top",
+        fast_dev_run=_config["fast_dev_run"],
+        val_check_interval=_config["val_check_interval"],
+        plugins=plugin_list,
+    )
+
+    if _config["loss_names"]["textmlm"] > 0:
+        for param in model.parameters():
+            param.requires_grad = False
+
+        for name, param in model.named_parameters():
+            for key in ["text_embeddings", "token_type_embeddings", "mlp_text", "norm2_text", "mlm_score", "relative_position_bias_table", "transformer.norm"]:
+                if key in name:
+                    param.requires_grad = True
+
+        for name, param in model.named_parameters():
+            rank_zero_info("{}\t{}".format(name, param.requires_grad))
+
+    if not _config["test_only"]:
+        trainer.fit(model, datamodule=dm)
+    else:
+        trainer.test(model, datamodule=dm)

vmlo/setup.py

+from setuptools import setup, find_packages
+
+setup(
+    name="vlmo",
+    packages=find_packages(
+        exclude=[".dfc", ".vscode", "dataset", "notebooks", "result", "scripts"]
+    ),
+    version="1.0.0",
+    license="MIT",
+    description="VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts",
+    author="Wenhui Wang",
+    author_email="wenwan@microsoft.com",
+    url="https://github.com/microsoft/unilm/tree/master/vlmo",
+    keywords=["vision and language pretraining"],
+    install_requires=["torch", "pytorch_lightning"],
+)

vmlo/train.sh

+#!/bin/bash/
+
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export USE_MIOPEN_BATCHNORM=1
+
+export INIT_CKPT=./pretrained_models/vlmo_base_patch16_224.pt
+
+python run.py with data_root=/public/home/chenych/datasets/coco2014/ num_gpus=4 num_nodes=1 "task_finetune_irtr_coco_base_image384" per_gpu_batchsize=64 load_path=$INIT_CKPT log_dir=./logs/
+
+# python run.py with data_root=<ARROW_ROOT> num_gpus=<NUM_GPUS> num_nodes=<NUM_NODES> "<TASK>" per_gpu_batchsize=<BS_FITS_YOUR_GPU> load_path="<VLMo_WEIGHT>" log_dir=<YOUR_OUTPUT_PATH>
\ No newline at end of file

vmlo/val.sh

+#!/bin/bash/
+
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export USE_MIOPEN_BATCHNORM=1
+
+python run.py with data_root=/public/home/chenych/datasets/coco2014/ num_gpus=1 num_nodes=1 "task_finetune_irtr_coco_base_image384" per_gpu_batchsize=64 load_path="./pretrained_models/vlmo_base_patch16_384_coco.pt" test_only=True
+
+# python run.py with data_root=<ARROW_ROOT> num_gpus=1 num_nodes=1 "<CONFIG_NAME>" per_gpu_batchsize=<BS_FITS_YOUR_GPU> load_path="<Finetuned_VLMo_WEIGHT>" test_only=True

vmlo/vlmo/config.py

+from sacred import Experiment
+
+ex = Experiment("VLMo")
+
+
+def _loss_names(d):
+    ret = {
+        "itm": 0, # image-text matching loss
+        "itc": 0, # image-text contrastive loss
+        "mlm": 0, # masked language modeling loss
+        "textmlm": 0, # text-only masked language modeling
+        "vqa": 0,
+        "nlvr2": 0,
+        "irtr": 0, # retrieval task ft
+    }
+    ret.update(d)
+    return ret
+
+
+@ex.config
+def config():
+    exp_name = "vlmo"
+    seed = 1
+    datasets = ["coco", "vg", "sbu", "gcc"]
+    loss_names = _loss_names({"itm": 1, "itc": 1, "mlm": 1})
+    batch_size = 1024  # this is a desired batch size; pl trainer will accumulate gradients when per step batch is smaller.
+
+    # Image setting
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    image_size = 224
+    draw_false_image = 0
+    image_only = False
+    text_only = False
+
+    # Text Setting
+    vqav2_label_size = 3129
+    max_text_len = 40
+    max_text_len_of_initckpt = 196
+    tokenizer = "bert-base-uncased"
+    vocab_size = 30522
+    whole_word_masking = False
+    mlm_prob = 0.15
+    draw_false_text = 0
+
+    # Transformer Setting
+    model_arch = "vlmo_base_patch16"
+    drop_path_rate = 0.1
+
+    # Optimizer Setting
+    optim_type = "adamw"
+    learning_rate = 1e-4
+    weight_decay = 0.01
+    decay_power = 1
+    max_epoch = 100
+    max_steps = 200000
+    warmup_steps = 0.1
+    end_lr = 0
+    lr_mult = 1  # multiply lr for downstream heads
+
+    # Downstream Setting
+    get_recall_metric = False
+    get_recall_rerank_metric = False
+    k_test = 32
+
+    # PL Trainer Setting
+    resume_from = None
+    fast_dev_run = False
+    val_check_interval = 1.0
+    test_only = False
+    use_sharded_training = False
+    resume_during_training = False
+
+    # below params varies with the environment
+    data_root = ""
+    log_dir = "result"
+    per_gpu_batchsize = 4  # you should define this manually with per_gpu_batch_size=#
+    num_gpus = 1
+    num_nodes = 1
+    load_path = ""
+    num_workers = 8
+    precision = 16
+
+
+# ----------------------- language pretraining config -----------------------
+
+
+@ex.named_config
+def task_textmlm_base():
+    exp_name = "textmlm_base"
+    datasets = ["wikibk"]
+    loss_names = _loss_names({"textmlm": 1})
+    batch_size = 1024
+    max_text_len = 196
+    learning_rate = 2e-4
+    whole_word_masking = True
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_textmlm_base_plus():
+    exp_name = "textmlm_base_plus"
+    datasets = ["wikibk"]
+    loss_names = _loss_names({"textmlm": 1})
+    batch_size = 1024
+    max_text_len = 196
+    learning_rate = 2e-4
+    whole_word_masking = True
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    model_arch = "vlmo_base_plus_patch16"
+
+
+# ----------------------- vision-language pretraining config -----------------------
+
+
+# Named configs for "task" which define datasets, loss_names and desired batch_size, warmup_steps, epochs, and exp_name
+@ex.named_config
+def task_mlm_itm_itc_base():
+    exp_name = "mlm_itm_itc_base"
+    datasets = ["coco", "vg", "sbu", "gcc"]
+    loss_names = _loss_names({"itm": 1, "mlm": 1, "itc": 1})
+    batch_size = 1024
+    whole_word_masking = True
+    learning_rate = 2e-4
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_mlm_itm_itc_base_plus():
+    exp_name = "mlm_itm_itc_base_plus"
+    datasets = ["coco", "vg", "sbu", "gcc"]
+    loss_names = _loss_names({"itm": 1, "mlm": 1, "itc": 1})
+    batch_size = 1024
+    whole_word_masking = True
+    learning_rate = 1e-4
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    model_arch = "vlmo_base_plus_patch16"
+
+
+@ex.named_config
+def task_mlm_itm_itc_large():
+    exp_name = "mlm_itm_itc_large"
+    datasets = ["coco", "vg", "sbu", "gcc"]
+    loss_names = _loss_names({"itm": 1, "mlm": 1, "itc": 1})
+    batch_size = 1024
+    whole_word_masking = True
+    learning_rate = 5e-5
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    model_arch = "vit_large_patch16_224"
+
+
+# ----------------------- NLVR2 fine-tuning configs -----------------------
+
+
+@ex.named_config
+def task_finetune_nlvr2_base():
+    exp_name = "finetune_nlvr2_base"
+    datasets = ["nlvr2"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"nlvr2": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 5e-5
+    val_transform_keys = ["square_transform"]
+    use_sharded_training=False
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_finetune_nlvr2_base_plus():
+    exp_name = "finetune_nlvr2_base_plus"
+    datasets = ["nlvr2"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"nlvr2": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 3e-5
+    drop_path_rate = 0.2
+    val_transform_keys = ["square_transform"]
+    use_sharded_training=False
+    model_arch = "vlmo_base_plus_patch16"
+
+
+@ex.named_config
+def task_finetune_nlvr2_base_image384():
+    exp_name = "finetune_nlvr2_base_image384"
+    datasets = ["nlvr2"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"nlvr2": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 5e-5
+    val_transform_keys = ["square_transform"]
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_finetune_nlvr2_base_plus_image384():
+    exp_name = "finetune_nlvr2_base_plus_image384"
+    datasets = ["nlvr2"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"nlvr2": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 3e-5
+    drop_path_rate = 0.2
+    val_transform_keys = ["square_transform"]
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_base_plus_patch16"
+
+
+@ex.named_config
+def task_finetune_nlvr2_large():
+    exp_name = "finetune_nlvr2_large"
+    datasets = ["nlvr2"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"nlvr2": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 3e-5
+    drop_path_rate = 0.15
+    val_transform_keys = ["square_transform"]
+    use_sharded_training=False
+    model_arch = "vlmo_large_patch16"
+
+
+@ex.named_config
+def task_finetune_nlvr2_large_image384():
+    exp_name = "finetune_nlvr2_large_image384"
+    datasets = ["nlvr2"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"nlvr2": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 3e-5
+    drop_path_rate = 0.15
+    val_transform_keys = ["square_transform"]
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_large_patch16"
+
+
+# ----------------------- VQAv2 Fine-tuning configs -----------------------
+
+
+@ex.named_config
+def task_finetune_vqa_base_image480():
+    exp_name = "finetune_vqa_base_image480"
+    datasets = ["vqa"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"vqa": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 3e-5
+    drop_path_rate = 0.15
+    val_transform_keys = ["square_transform"]
+    lr_mult = 20
+    image_size = 480
+    use_sharded_training=False
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_finetune_vqa_base_plus_image480():
+    exp_name = "finetune_vqa_base_plus_image480"
+    datasets = ["vqa"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"vqa": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 3e-5
+    drop_path_rate = 0.15
+    val_transform_keys = ["square_transform"]
+    lr_mult = 20
+    image_size = 480
+    use_sharded_training=False
+    model_arch = "vlmo_base_plus_patch16"
+
+
+@ex.named_config
+def task_finetune_vqa_large_image480():
+    exp_name = "finetune_vqa_large_image480"
+    datasets = ["vqa"]
+    train_transform_keys = ["square_transform_randaug"]
+    loss_names = _loss_names({"vqa": 1})
+    batch_size = 128
+    max_epoch = 10
+    max_steps = None
+    warmup_steps = 0.1
+    learning_rate = 1.5e-5
+    drop_path_rate = 0.15
+    val_transform_keys = ["square_transform"]
+    lr_mult = 20
+    image_size = 480
+    use_sharded_training=False
+    model_arch = "vlmo_large_patch16"
+
+
+# ----------------------- F30K IR/TR Fine-tuning configs -----------------------
+
+
+@ex.named_config
+def task_finetune_irtr_f30k_base():
+    exp_name = "finetune_irtr_f30k_base"
+    datasets = ["f30k"]
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    loss_names = _loss_names({"irtr": 1.0})
+    batch_size = 3072
+    max_epoch = 50
+    max_steps = 1500
+    warmup_steps = 150
+    get_recall_metric = True
+    learning_rate = 3e-5
+    drop_path_rate = 0.15
+    use_sharded_training=False
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_finetune_irtr_f30k_base_image384():
+    exp_name = "finetune_irtr_f30k_base_image384"
+    datasets = ["f30k"]
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    loss_names = _loss_names({"irtr": 1.0})
+    batch_size = 3072
+    max_epoch = 50
+    max_steps = 1500
+    warmup_steps = 150
+    get_recall_metric = True
+    learning_rate = 3e-5
+    drop_path_rate = 0.15
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_finetune_irtr_f30k_base_plus_image384():
+    exp_name = "finetune_irtr_f30k_base_plus_image384"
+    datasets = ["f30k"]
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    loss_names = _loss_names({"irtr": 1.0})
+    batch_size = 3072
+    max_epoch = 50
+    max_steps = 1500
+    warmup_steps = 150
+    get_recall_metric = True
+    learning_rate = 3e-5
+    drop_path_rate = 0.2
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_base_plus_patch16"
+
+
+@ex.named_config
+def task_finetune_irtr_f30k_large_image384():
+    exp_name = "finetune_irtr_f30k_large_image384"
+    datasets = ["f30k"]
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    loss_names = _loss_names({"irtr": 1.0})
+    batch_size = 3072
+    max_epoch = 50
+    max_steps = 1500
+    warmup_steps = 150
+    get_recall_metric = True
+    learning_rate = 2e-5
+    drop_path_rate = 0.2
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_large_patch16"
+
+
+# ----------------------- COCO IR/TR Fine-tuning configs -----------------------
+
+
+@ex.named_config
+def task_finetune_irtr_coco_base_image384():
+    exp_name = "finetune_irtr_coco_base_image384"
+    datasets = ["coco"]
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    loss_names = _loss_names({"irtr": 1.0})
+    batch_size = 3072
+    max_epoch = 50
+    max_steps = 3000
+    warmup_steps = 300
+    get_recall_metric = True
+    learning_rate = 3e-5
+    drop_path_rate = 0.2
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_base_patch16"
+
+
+@ex.named_config
+def task_finetune_irtr_coco_base_plus_image384():
+    exp_name = "finetune_irtr_coco_base_plus_image384"
+    datasets = ["coco"]
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    loss_names = _loss_names({"irtr": 1.0})
+    batch_size = 3072
+    max_epoch = 50
+    max_steps = 3000
+    warmup_steps = 300
+    get_recall_metric = True
+    learning_rate = 3e-5
+    drop_path_rate = 0.2
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_base_plus_patch16"
+
+
+@ex.named_config
+def task_finetune_irtr_coco_large_image384():
+    exp_name = "finetune_irtr_coco_large_image384"
+    datasets = ["coco"]
+    train_transform_keys = ["square_transform_randaug"]
+    val_transform_keys = ["square_transform"]
+    loss_names = _loss_names({"irtr": 1.0})
+    batch_size = 3072
+    max_epoch = 50
+    max_steps = 3000
+    warmup_steps = 300
+    get_recall_metric = True
+    learning_rate = 2e-5
+    drop_path_rate = 0.2
+    image_size = 384
+    use_sharded_training=False
+    model_arch = "vlmo_large_patch16"
+
+
+# ----------------------- Other configs -----------------------
+
+
+# Named configs for "etc" which are orthogonal to "env" and "task", need to be added at the end
+@ex.named_config
+def step1_5k():
+    max_epoch = 100
+    warmup_steps = 150
+    max_steps = 1500
+
+
+@ex.named_config
+def step3k():
+    max_epoch = 100
+    warmup_steps = 300
+    max_steps = 3000
+
+
+@ex.named_config
+def step200k():
+    max_epoch = 200
+    warmup_steps = 2500
+    max_steps = 200000
+
+
+@ex.named_config
+def step500k():
+    max_epoch = 500
+    warmup_steps = 2500
+    max_steps = 500000
\ No newline at end of file

vmlo/vlmo/datamodules/__init__.py

+from .vg_caption_datamodule import VisualGenomeCaptionDataModule
+from .f30k_caption_karpathy_datamodule import F30KCaptionKarpathyDataModule
+from .coco_caption_karpathy_datamodule import CocoCaptionKarpathyDataModule
+from .conceptual_caption_datamodule import ConceptualCaptionDataModule
+from .sbu_datamodule import SBUCaptionDataModule
+from .wikibk_datamodule import WikibkDataModule
+from .vqav2_datamodule import VQAv2DataModule
+from .nlvr2_datamodule import NLVR2DataModule
+
+_datamodules = {
+    "vg": VisualGenomeCaptionDataModule,
+    "f30k": F30KCaptionKarpathyDataModule,
+    "coco": CocoCaptionKarpathyDataModule,
+    "gcc": ConceptualCaptionDataModule,
+    "sbu": SBUCaptionDataModule,
+    "wikibk": WikibkDataModule,
+    "vqa": VQAv2DataModule,
+    "nlvr2": NLVR2DataModule,
+}

vmlo/vlmo/datamodules/coco_caption_karpathy_datamodule.py

+from vlmo.datasets import CocoCaptionKarpathyDataset
+from .datamodule_base import BaseDataModule
+
+
+class CocoCaptionKarpathyDataModule(BaseDataModule):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @property
+    def dataset_cls(self):
+        return CocoCaptionKarpathyDataset
+
+    @property
+    def dataset_cls_no_false(self):
+        return CocoCaptionKarpathyDataset
+
+    @property
+    def dataset_name(self):
+        return "coco"

vmlo/vlmo/datamodules/conceptual_caption_datamodule.py

+from vlmo.datasets import ConceptualCaptionDataset
+from .datamodule_base import BaseDataModule
+
+
+class ConceptualCaptionDataModule(BaseDataModule):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @property
+    def dataset_cls(self):
+        return ConceptualCaptionDataset
+
+    @property
+    def dataset_name(self):
+        return "gcc"

vmlo/vlmo/datamodules/datamodule_base.py

+import torch
+
+from pytorch_lightning import LightningDataModule
+from torch.utils.data import DataLoader
+from transformers import (
+    DataCollatorForLanguageModeling,
+    DataCollatorForWholeWordMask,
+    BertTokenizer,
+)
+
+
+def get_pretrained_tokenizer(from_pretrained):
+    if torch.distributed.is_initialized():
+        if torch.distributed.get_rank() == 0:
+            BertTokenizer.from_pretrained(
+                from_pretrained, do_lower_case="uncased" in from_pretrained
+            )
+        torch.distributed.barrier()
+    return BertTokenizer.from_pretrained(
+        from_pretrained, do_lower_case="uncased" in from_pretrained
+    )
+
+
+class BaseDataModule(LightningDataModule):
+    def __init__(self, _config):
+        super().__init__()
+
+        self.data_dir = _config["data_root"]
+
+        self.num_workers = _config["num_workers"]
+        self.batch_size = _config["per_gpu_batchsize"]
+        self.eval_batch_size = self.batch_size
+
+        self.image_size = _config["image_size"]
+        self.max_text_len = _config["max_text_len"]
+        self.draw_false_image = _config["draw_false_image"]
+        self.draw_false_text = _config["draw_false_text"]
+        self.image_only = _config["image_only"]
+        self.text_only = _config["text_only"]
+
+        self.train_transform_keys = (
+            ["default_train"]
+            if len(_config["train_transform_keys"]) == 0
+            else _config["train_transform_keys"]
+        )
+
+        self.val_transform_keys = (
+            ["default_val"]
+            if len(_config["val_transform_keys"]) == 0
+            else _config["val_transform_keys"]
+        )
+
+        tokenizer = _config["tokenizer"]
+        self.tokenizer = get_pretrained_tokenizer(tokenizer)
+        self.vocab_size = self.tokenizer.vocab_size
+
+        collator = (
+            DataCollatorForWholeWordMask
+            if _config["whole_word_masking"]
+            else DataCollatorForLanguageModeling
+        )
+
+        self.mlm_collator = collator(
+            tokenizer=self.tokenizer, mlm=True, mlm_probability=_config["mlm_prob"]
+        )
+        self.setup_flag = False
+
+    @property
+    def dataset_cls(self):
+        raise NotImplementedError("return tuple of dataset class")
+
+    @property
+    def dataset_name(self):
+        raise NotImplementedError("return name of dataset")
+
+    def set_train_dataset(self):
+        self.train_dataset = self.dataset_cls(
+            self.data_dir,
+            self.train_transform_keys,
+            split="train",
+            image_size=self.image_size,
+            max_text_len=self.max_text_len,
+            draw_false_image=self.draw_false_image,
+            draw_false_text=self.draw_false_text,
+            image_only=self.image_only,
+        )
+
+    def set_val_dataset(self):
+        self.val_dataset = self.dataset_cls(
+            self.data_dir,
+            self.val_transform_keys,
+            split="val",
+            image_size=self.image_size,
+            max_text_len=self.max_text_len,
+            draw_false_image=self.draw_false_image,
+            draw_false_text=self.draw_false_text,
+            image_only=self.image_only,
+        )
+
+        if hasattr(self, "dataset_cls_no_false"):
+            self.val_dataset_no_false = self.dataset_cls_no_false(
+                self.data_dir,
+                self.val_transform_keys,
+                split="val",
+                image_size=self.image_size,
+                max_text_len=self.max_text_len,
+                draw_false_image=0,
+                draw_false_text=0,
+                image_only=self.image_only,
+            )
+
+    def make_no_false_val_dset(self, image_only=False):
+        return self.dataset_cls_no_false(
+            self.data_dir,
+            self.val_transform_keys,
+            split="val",
+            image_size=self.image_size,
+            max_text_len=self.max_text_len,
+            draw_false_image=0,
+            draw_false_text=0,
+            image_only=image_only,
+        )
+
+    def make_no_false_test_dset(self, image_only=False):
+        return self.dataset_cls_no_false(
+            self.data_dir,
+            self.val_transform_keys,
+            split="test",
+            image_size=self.image_size,
+            max_text_len=self.max_text_len,
+            draw_false_image=0,
+            draw_false_text=0,
+            image_only=image_only,
+        )
+
+    def set_test_dataset(self):
+        self.test_dataset = self.dataset_cls(
+            self.data_dir,
+            self.val_transform_keys,
+            split="test",
+            image_size=self.image_size,
+            max_text_len=self.max_text_len,
+            draw_false_image=self.draw_false_image,
+            draw_false_text=self.draw_false_text,
+            image_only=self.image_only,
+        )
+
+    def setup(self, stage):
+        if not self.setup_flag:
+            self.set_train_dataset()
+            self.set_val_dataset()
+            self.set_test_dataset()
+
+            self.train_dataset.tokenizer = self.tokenizer
+            self.val_dataset.tokenizer = self.tokenizer
+            self.test_dataset.tokenizer = self.tokenizer
+
+            self.setup_flag = True
+
+    def train_dataloader(self):
+        loader = DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            collate_fn=self.train_dataset.collate,
+        )
+        return loader
+
+    def val_dataloader(self):
+        loader = DataLoader(
+            self.val_dataset,
+            batch_size=self.eval_batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            collate_fn=self.val_dataset.collate,
+        )
+        return loader
+
+    def test_dataloader(self):
+        loader = DataLoader(
+            self.test_dataset,
+            batch_size=self.eval_batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            collate_fn=self.test_dataset.collate,
+        )
+        return loader

vmlo/vlmo/datamodules/f30k_caption_karpathy_datamodule.py

+from vlmo.datasets import F30KCaptionKarpathyDataset
+from .datamodule_base import BaseDataModule
+
+
+class F30KCaptionKarpathyDataModule(BaseDataModule):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @property
+    def dataset_cls(self):
+        return F30KCaptionKarpathyDataset
+
+    @property
+    def dataset_cls_no_false(self):
+        return F30KCaptionKarpathyDataset
+
+    @property
+    def dataset_name(self):
+        return "f30k"

vmlo/vlmo/datamodules/multitask_datamodule.py

+import functools
+
+from pytorch_lightning import LightningDataModule
+from torch.utils.data import DataLoader
+from torch.utils.data.dataset import ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from . import _datamodules
+
+
+class MTDataModule(LightningDataModule):
+    def __init__(self, _config, dist=False):
+        datamodule_keys = _config["datasets"]
+        assert len(datamodule_keys) > 0
+
+        super().__init__()
+
+        self.dm_keys = datamodule_keys
+        self.dm_dicts = {key: _datamodules[key](_config) for key in datamodule_keys}
+        self.dms = [v for k, v in self.dm_dicts.items()]
+
+        self.batch_size = self.dms[0].batch_size
+        self.vocab_size = self.dms[0].vocab_size
+        self.num_workers = self.dms[0].num_workers
+
+        self.dist = dist
+
+    def prepare_data(self):
+        for dm in self.dms:
+            dm.prepare_data()
+
+    def setup(self, stage):
+        for dm in self.dms:
+            dm.setup(stage)
+
+        self.train_dataset = ConcatDataset([dm.train_dataset for dm in self.dms])
+        self.val_dataset = ConcatDataset([dm.val_dataset for dm in self.dms])
+        self.test_dataset = ConcatDataset([dm.test_dataset for dm in self.dms])
+        self.tokenizer = self.dms[0].tokenizer
+
+        self.collate = functools.partial(
+            self.dms[0].train_dataset.collate, mlm_collator=self.dms[0].mlm_collator,
+        )
+
+        if self.dist:
+            self.train_sampler = DistributedSampler(self.train_dataset, shuffle=True)
+            self.val_sampler = DistributedSampler(self.val_dataset, shuffle=True)
+            self.test_sampler = DistributedSampler(self.test_dataset, shuffle=False)
+        else:
+            self.train_sampler = None
+            self.val_sampler = None
+            self.test_sampler = None
+
+    def train_dataloader(self):
+        loader = DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            sampler=self.train_sampler,
+            num_workers=self.num_workers,
+            collate_fn=self.collate,
+        )
+        return loader
+
+    def val_dataloader(self, batch_size=None):
+        loader = DataLoader(
+            self.val_dataset,
+            batch_size=batch_size if batch_size is not None else self.batch_size,
+            sampler=self.val_sampler,
+            num_workers=self.num_workers,
+            collate_fn=self.collate,
+        )
+        return loader
+
+    def test_dataloader(self):
+        loader = DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            sampler=self.test_sampler,
+            num_workers=self.num_workers,
+            collate_fn=self.collate,
+        )
+        return loader

vmlo/vlmo/datamodules/nlvr2_datamodule.py

+from vlmo.datasets import NLVR2Dataset
+from .datamodule_base import BaseDataModule
+
+
+class NLVR2DataModule(BaseDataModule):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @property
+    def dataset_cls(self):
+        return NLVR2Dataset
+
+    @property
+    def dataset_name(self):
+        return "nlvr2"

vmlo/vlmo/datamodules/sbu_datamodule.py

+from vlmo.datasets import SBUCaptionDataset
+from .datamodule_base import BaseDataModule
+
+
+class SBUCaptionDataModule(BaseDataModule):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @property
+    def dataset_cls(self):
+        return SBUCaptionDataset
+
+    @property
+    def dataset_name(self):
+        return "sbu"