--- tags: - clip - vision - text language: multilingual license: mit --- # MultiLingual CLIP Multilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages. # Model Architecture Multilingual CLIP was built using [OpenAI CLIP](https://github.com/openai/CLIP) model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced their text encoder (Transformer) with a Mulilingual Text Encoder ([XLM-Roberta](https://huggingface.co/xlm-roberta-large)) and a configurable number of projection heads, as seen below: ![Model Architecture](https://challengepost-s3-challengepost.netdna-ssl.com/photos/production/software_photos/001/858/046/datas/gallery.jpg) The model was trained in a distributed fashion on 16 Habana Gaudi Accelerators and with mixed Precision in two phases (using COCO Dataset for phase 1 and Google Conceptual Captions for phase 2). The training pipeline was built using PyTorch, PyTorch Lightning, and Distributed Data Parallel. # Datasets Three datasets have been used for building the model. COCO captions was used for training phase 1 and Google Conceptual Captions was used for training phase 2. Unsplash dataset was used for testing and inference. ## COCO Captions COCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. The COCO captions dataset has around ~85000 images and captions pairs. Run the following to download the dataset: ```bash ./download_coco.sh ``` This dataset was used for the first pre-training phase. ## Google Conceptual Captions Conceptual Captions is a dataset consisting of ~3.3 million images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. Download the datasets urls/captions from [here](https://storage.cloud.google.com/gcc-data/Train/GCC-training.tsv?_ga=2.191230122.-1896153081.1529438250) as save it to `datasets/googlecc/googlecc.tsv`. The full dataset has over 3 million images, but you can select a subset by loading the `googlecc.tsv` file and saving only the number of rows you want (I have used 1 million images for training). Then run the following commands to download each image on the `googlecc.tsv` file: ```bash npm install node download_build_googlecc.js ``` This dataset was used for the second pre-training phase. ## Unplash This dataset was used as the test set during inference. Run `python3.8 download_unsplash.py` to download the dataset. # Training ![Training phase 1](https://challengepost-s3-challengepost.netdna-ssl.com/photos/production/software_photos/001/858/047/datas/gallery.jpg) ![Training phase 2](https://challengepost-s3-challengepost.netdna-ssl.com/photos/production/software_photos/001/858/048/datas/gallery.jpg) ## Setup Create two Habana instances ([AWS EC2 DL1](https://aws.amazon.com/ec2/instance-types/dl1/)) using [Habana® Deep Learning Base AMI (Ubuntu 20.04)](https://aws.amazon.com/marketplace/pp/prodview-fw46rwuxrtfse) Create the PyTorch docker container running: ```bash docker run --name pytorch -td --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.2.0/ubuntu20.04/habanalabs/pytorch-installer-1.10.0:1.2.0-585 ``` Enter the docker image by running: ``` docker exec -it pytorch /bin/bash ``` #### Setup password-less ssh between all connected servers 1. Configure password-less ssh between all nodes: Do the following in all the nodes' docker sessions: ```bash mkdir ~/.ssh cd ~/.ssh ssh-keygen -t rsa -b 4096 ``` Copy id_rsa.pub contents from every node's docker to every other node's docker's ~/.ssh/authorized_keys (all public keys need to be in all hosts' authorized_keys): ```bash cat id_rsa.pub > authorized_keys vi authorized_keys ``` Copy the contents from inside to other systems. Paste all hosts' public keys in all hosts' “authorized_keys” file. 2. On each system: Add all hosts (including itself) to known_hosts. The IP addresses used below are just for illustration: ```bash ssh-keyscan -p 3022 -H $IP1 >> ~/.ssh/known_hosts ssh-keyscan -p 3022 -H $IP2 >> ~/.ssh/known_hosts ``` 3. Change Docker SSH port to 3022 ```bash sed -i 's/#Port 22/Port 3022/g' /etc/ssh/sshd_config sed -i 's/#PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config service ssh restart ``` [Allow all TCP](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html) traffic between the nodes on AWS Clone the git repo: ```bash git clone https://github.com/gzomer/clip-multilingual ``` Create environment: ```bash python3.8 -m venv .env ``` Install requirements: ```bash python3.8 -r requirements.txt ``` Activate environment ```bash source .env/bin/activate ``` ## Training params Learning rate: 1e-3 Batch size: 64 Phase 1 - Epochs: 100 Phase 2 - Epochs: 15 ## Train script arguments ``` --dataset-num-workers Number of workers (default: 8) --dataset-type Dataset type (coco or googlecc) (default: coco) --dataset-dir Dataset dir (default: ./datasets/coco/) --dataset-subset-size Load only a subset of the dataset (useful for debugging) --dataset-train-split Dataset train split (default: 0.8) --train-device Type of device to use (default: hpu) --distributed-num-nodes Number of nodes (machines) (default: 2) --distributed-parallel-devices Number of parallel devices per node (default: 8) --distributed-master-address Master node IP address --distributed-master-port Master node port (default: 12345) --distributed-bucket-cap-mb DDP bucket cap MB (default: 200) --checkpoint-dir Model checkpoint dir (default: ./models) --checkpoint-save-every-n Save every n epochs (default: 1) --checkpoint-load-vision-path Load vision encoder checkpoint --checkpoint-load-text-path Load text encoder checkpoint --model-visual-name Which visual model to use (default: RN50x4) --model-textual-name Which textual model to use (default: xlm-roberta-base) --hyperparam-num-layers Number of layers (default: 3) --hyperparam-lr Model learning rate (default: 0.001) --hyperparam-epochs Max epochs (default: 100) --hyperparam-precision Precision (default: 16) --hyperparam-batch-size Batch size (default: 64) --wandb-project W&B project name (default: clip) --wandb-enabled W&B is enabled? (default: True) ``` ## Habana Gaudi - 8 accelerators ### Phase 1 training ```bash python3.8 train.py --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 1 ``` ### Phase 2 training ```bash python3.8 train.py --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 1 --hyperparam-epochs 15 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --checkpoint-dir ./models_phase2 ``` ## Habana Gaudi - 16 accelerators (multi-server training) Change the master IP address based on your instances (use local IP, not public IP). ### Phase 1 training ```bash NODE_RANK=0 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 ``` ```bash NODE_RANK=1 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 ``` ### Phase 2 training ```bash NODE_RANK=0 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 --hyperparam-epochs 10 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --checkpoint-dir ./models_phase2 ``` ```bash NODE_RANK=1 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 --hyperparam-epochs 15 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --checkpoint-dir ./models_phase2 ``` ## Other devices If you don't have access to a Habana Gaudi accelerator yet, you can also train on CPU/GPU, although it will be way slower. To train on CPU, just pass `--train-device=cpu` and on GPU `--train-device=cuda` to the `train.py` script. # Inference ## Loading pre-trained model from Hugging Face HUB ```python from models import create_and_load_from_hub model = create_and_load_from_hub() ``` ## Loading model from local checkpoint ```python from models import MultiLingualCLIP, load_model text_checkpoint_path = '/path/to/text model checkpoint' vision_checkpoint_path = '/path/to/vision model checkpoint' model = MultiLingualCLIP(num_layers=3) load_model(model, vision_checkpoint_path, text_checkpoint_path) ``` ## Generate embeddings Run the following (after downloading Unplash dataset): `python3.8 ./generate_embeddings.py` ## Searching images ```python import numpy as np from search import MultiLingualSearch images_embeddings = np.load('/path/to/images_embeddings') images_data = [...] # List of image info for each row of the embeddings. For instance, it could be a list of urls, filepaths, ids. They will be returned when calling the search function semantic_search = MultiLingualSearch(model, images_embeddings, images_data) results = semantic_search.search('विद्यालय में') # Means at school print(results) ``` ```json [{"image": "https://images.unsplash.com/photo-1557804506-669a67965ba0?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHwxM3x8bWVldGluZ3N8ZW58MHx8fHwxNjQ1NjA2MjQz&ixlib=rb-1.2.1&q=80&w=400", "prob": 0.2461608648300171}, {"image": "https://images.unsplash.com/photo-1558403194-611308249627?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHwyMXx8cGVvcGxlJTIwd29ya2luZ3xlbnwwfHx8fDE2NDU2MDMyMjE&ixlib=rb-1.2.1&q=80&w=400", "prob": 0.16881239414215088}, {"image": "https://images.unsplash.com/photo-1531497865144-0464ef8fb9a9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHw4Nnx8cGVvcGxlJTIwd29ya2luZ3xlbnwwfHx8fDE2NDU2MDY5ODc&ixlib=rb-1.2.1&q=80&w=400", "prob": 0.14744874835014343}, {"image": "https://images.unsplash.com/photo-1561089489-f13d5e730d72?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHw5MHx8ZWR1Y2F0aW9ufGVufDB8fHx8MTY0NTYwNjk1Nw&ixlib=rb-1.2.1&q=80&w=400", "prob": 0.095176100730896}, {"image": "https://images.unsplash.com/photo-1580582932707-520aed937b7b?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHwxMnx8ZWR1Y2F0aW9ufGVufDB8fHx8MTY0NTYwMzIwMA&ixlib=rb-1.2.1&q=80&w=400", "prob": 0.05218643322587013}] ```