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Guide To 12-in-1: A Multi-Task Vision And Language Representation Learning Model

Guide To 12-in-1: A Multi-Task Vision And Language Representation Learning Model



Visual Recognition and Language Understanding are two of the challenging tasks in the domain of Artificial Intelligence. A great deal of vision-and-language research focuses on a small number of independent tasks of different types. Also, it supports an isolated analysis of each of the datasets involved. But the visually dependent language comprehension skills needed for these tasks to succeed overlap significantly. 

12-in-1, a multi-task vision and language representation learning approach discussed in this article is a single model run on 12 different datasets. It performs four major vision-and-language tasks on its own – visual question answering, caption-based image retrieval, grounding referring expressions and multi-modal verification.

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The 12-in-1 model was proposed by Jiasen Lu, Vedanuj Goswami, Marcus Rohbach, Devi Parikh and Stefan Lee – researchers from Facebook AI Research, Oregon State University and Georgia Institute of Technology in June 2020.

Traditional models

Language is an interface for visual reasoning tasks. The field of vision-and-language research combines vision and language to perform specialized tasks such as caption generation, each of which is supported by a few datasets. Conventional models used in this field employ common architectures to learn general Visio-linguistic representations and then fine-tune for specifically supported datasets. Such models are task-specific. However, the associations between language and vision are common across many such tasks. For instance, the task of learning to ground the expression “a yellow ball” requires the same concepts as answering the question “What colour is the ball?”

Overview of 12-in-1 model

12-in-1 is a multi-task model for discriminative vision-and-language tasks based on the ViLBERT (Vision and Language BERT) model. If you are unfamiliar with the BERT and the ViLBERT model, you may refer to the following links before proceeding:

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Task-Groups in 12-in-1

The 12 datasets used by the model perform cover a variety of tasks which have been grouped into 4 categories as follows:

  1. Vocab based VQA
  • Given an image and a natural-language question, the task is to select an answer from a fixed vocabulary.
  • Datasets – (i)VQAv2  (ii)GQA  (iii)Visual Genome (VG) QA 
  1. Image retrieval
  • Given a caption and a pool of images, the task is to retrieve the target image that is best described by the caption.
  • Datasets – (i)COCO  (ii)Flickr30K
  1. Referring expressions
  • Given a natural language expression and an image, the task is to identify the target region that is referred to by expression (can be as simple as a noun phrase or as complex as a multi-round dialog).
  • Datasets – (i)RefCOCO(+/g)   (ii)Visual7W   (iii)GuessWhat
  1. Multi-modal verification
  • Given one or more images and a natural language statement, the task is to judge the correctness or predict their semantic relationship. 
  • Datasets – (i)NLVR   (ii)SNLI-VE

Base architecture of 12-in-1

The ViLBERT model forms the basis of the 12-in-1 multi-task model.  ViLBERT takes as input an image I and text segment Q. The model then outputs embeddings for each input. Internally, ViLBERT uses two BERT-type models – one working on text segments and the other on image regions. 

Clean V&L multi-task setup

Since many V&L (vision-and-language) tasks overlap in terms of images, a clean setup has been designed to avoid information leakage from annotations from other tasks. The test images are removed from the train/validation set for all the tasks. The test images are thus left unmodified and the size of training data gets significantly reduced.

As shown in the above figure, the single 12-in-1 model performs a variety of tasks – caption and image retrieval, question answering, grounding phrases, guessing image regions based on a dialog, verifying facts about a pair of images, natural language inferences from an image, etc. It enables the exchange of information between images and text segments. 

Performance comparison of 12-in-1

Compared to a set of independent state-of-the-art models each used for a specific V&L task, the improved ViLBERT model represents a reduction from ∼3 billion parameters to ∼270 million. It has also been found to have improved the average performance by 2.05 points. Multi-task training is useful even in cases of single task scenarios. Fine-tuning the multi-task model for single tasks gives better results than the baseline single-task trained models.

Practical implementation of 12-in-1

Here’s a demonstration of the multi-task model implemented using Python 3 in Google colab.

The steps to be followed for the implementation are as follows:

1) Clone the GitHub repository

!git clone ''

2)Import the required libraries and classes.

 import sys
 import os
 import torch
 import yaml  #for data serialization 

Here we have used easydict Python library which allows dictionary values to be used as attributes. (weblink

from easydict import EasyDict as edict 

The class PreTrainedTokenizer of PyTorch has common methods for loading/saving a tokenizer. Learn about PyTorch transformers from here.

from pytorch_transformers.tokenization_bert import BertTokenizer

The ConceptCapLoaderTrain and ConceptCapLoaderVal classes have been defined here. The former one combines a dataset and a sampler and provides single or multi-process iterators over the training dataset. The latter class does the same for the validation set.

from vilbert.datasets import ConceptCapLoaderTrain, ConceptCapLoaderVal

The configuration parameters and tasks to be done by the BERT model have been defined in the following imported classes

from vilbert.vilbert import VILBertForVLTasks, BertConfig, BertForMultiModalPreTraining

The LoadDatasetEval class loads the dataset for evaluating the model.

 from vilbert.task_utils import LoadDatasetEval
 import numpy as np
 import matplotlib.pyplot as plt
 import PIL (imaging library PIL to deal with the image sections) 

Here, we have used Mask R-CNN model for object instance segmentation.

 from maskrcnn_benchmark.config import cfg
 from maskrcnn_benchmark.layers import nms
 from maskrcnn_benchmark.modeling.detector import build_detection_model
 from maskrcnn_benchmark.structures.image_list import to_image_list
 from maskrcnn_benchmark.utils.model_serialization import load_state_dict
 from PIL import Image
 import cv2  #OpenCV library
 import argparse  #argparse library for parsing the text segments
 import glob  # glob library for unix-style pathname expansion
 from types import SimpleNamespace 
  #types module used for dynamic type creation for built-in types
 import pdb  #Python debugger 

3)Define feature extractor

 class FeatureExtractor:
     MAX_SIZE = 1333
     MIN_SIZE = 800
     def __init__(self):
         self.args = self.get_parser()
         self.detection_model = self._build_detection_model()
     def get_parser(self):        
         parser = SimpleNamespace(model_file= 

4) Set configuration path for the ResNet model.

         return parser 

5)Build the detection model

 def _build_detection_model(self):
         #Build the model using the defined configurations
         model = build_detection_model(cfg)
         checkpoint = torch.load(self.args.model_file, map_location=torch.device("cpu"))
         #Know about the torch.load() function here.
        #Load model’s parameter dictionary using a deserialized state_dic. (load_state_dict())
         load_state_dict(model, checkpoint.pop("model"))"cuda")
         return model 

6)Perform image transformation

 def _image_transform(self, path):
         img =
         im = np.array(img).astype(np.float32)
         # IndexError: too many indices for array, grayscale images
         if len(im.shape) < 3:
             im = np.repeat(im[:, :, np.newaxis], 3, axis=2)
         im = im[:, :, ::-1]
         im -= np.array([102.9801, 115.9465, 122.7717])
         im_shape = im.shape
         im_height = im_shape[0]
         im_width = im_shape[1]
         im_size_min = np.min(im_shape[0:2])
         im_size_max = np.max(im_shape[0:2])
         # Scale based on minimum size
         im_scale = self.MIN_SIZE / im_size_min
         # Prevent the biggest axis from being more than max_size
         # If bigger, scale it down
         if np.round(im_scale * im_size_max) > self.MAX_SIZE:
             im_scale = self.MAX_SIZE / im_size_max
         im = cv2.resize(
             im, None, None, fx=im_scale, fy=im_scale,   
         img = torch.from_numpy(im).permute(2, 0, 1)
         im_info = {"width": im_width, "height": im_height}
         return img, im_scale, im_info 

7) Define the feature extraction process.

 def _process_feature_extraction(
         self, output, im_scales, im_infos, feature_name="fc6", conf_thresh=0
         batch_size = len(output[0]["proposals"])
        #number of bounding boxes per detected image
         n_boxes_per_image = [len(boxes) for boxes in output[0]["proposals"]]
         score_list = output[0]["scores"].split(n_boxes_per_image)
         score_list = [torch.nn.functional.softmax(x, -1) for x in score_list]
         feats = output[0][feature_name].split(n_boxes_per_image)
         cur_device = score_list[0].device
         feat_list = []
         info_list = []
         for i in range(batch_size):
             dets = output[0]["proposals"][i].bbox / im_scales[i]
             scores = score_list[i]
             max_conf = torch.zeros((scores.shape[0])).to(cur_device)
             conf_thresh_tensor = torch.full_like(max_conf, conf_thresh)
             start_index = 1
             # Column 0 of the scores matrix is for the background class
             if self.args.background:
                 start_index = 0
             for cls_ind in range(start_index, scores.shape[1]):
                 cls_scores = scores[:, cls_ind]
                 keep = nms(dets, cls_scores, 0.5)
                 max_conf[keep] = torch.where(
 # Better than max one till now and minimally greater than conf_thresh
                     (cls_scores[keep] > max_conf[keep])
                     & (cls_scores[keep] > conf_thresh_tensor[keep]),
       sorted_scores, sorted_indices = torch.sort(max_conf, descending=True)
      num_boxes = (sorted_scores[: self.args.num_features] != 0).sum()
      keep_boxes = sorted_indices[: self.args.num_features]
     bbox = output[0]["proposals"][i][keep_boxes].bbox / im_scales[i] 

 8)Predict the class label using the scores

    #Return the indices of the maximum value of all elements in the input tensor using   
           torch.argmax() and torch.max()
             objects = torch.argmax(scores[keep_boxes][start_index:], dim=1)
             cls_prob = torch.max(scores[keep_boxes][start_index:], dim=1)
                     "bbox": bbox.cpu().numpy(),
                     "num_boxes": num_boxes.item(),
                     "objects": objects.cpu().numpy(),
                     "image_width": im_infos[i]["width"],
                     "image_height": im_infos[i]["height"],
                     "cls_prob": scores[keep_boxes].cpu().numpy(),
         return feat_list, info_list 

9)Get Detectron model’s features

 def get_detectron_features(self, image_paths):
         img_tensor, im_scales, im_infos = [], [], []
         for image_path in image_paths:
             im, im_scale, im_info = self._image_transform(image_path)
         /* Image dimensions should be divisible by 32, to allow convolutions
          in detector to work*/
         current_img_list = to_image_list(img_tensor, size_divisible=32)
         current_img_list ="cuda")
         with torch.no_grad():
             output = self.detection_model(current_img_list)
         feat_list = self._process_feature_extraction(
         return feat_list
 def _chunks(self, array, chunk_size):
         for i in range(0, len(array), chunk_size):
             yield array[i : i + chunk_size] 

10)Save the extracted features

 def _save_feature(self, file_name, feature, info):
         file_base_name = os.path.basename(file_name)
         file_base_name = file_base_name.split(".")[0]
         info["image_id"] = file_base_name
         info["features"] = feature.cpu().numpy()
         file_base_name = file_base_name + ".npy", file_base_name),     

11) Perform tokenization and detokenization of the text segments

 def tokenize_batch(batch):
     return [tokenizer.convert_tokens_to_ids(sent) for sent in batch]
 def untokenize_batch(batch):
     return [tokenizer.convert_ids_to_tokens(sent) for sent in batch]
 def detokenize(sent):
     """ Roughly detokenizes (mainly undoes wordpiece) """
     new_sent = []
     for i, tok in enumerate(sent):
         if tok.startswith("##"):
             new_sent[len(new_sent) - 1] = new_sent[len(new_sent) - 1] + tok[2:]
     return new_sent
 #Print the tokenization results
 def printer(sent, should_detokenize=True):
     if should_detokenize:
         sent = detokenize(sent)[1:-1]
     print(" ".join(sent))
 # write arbitrary string for a given sentence. 
 import _pickle as cPickle 

12)Define a method to make prediction

 def prediction(question, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask, task_tokens, ):
     vil_prediction, vil_prediction_gqa, vil_logit, vil_binary_prediction, vil_tri_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit, attn_data_list = model(
         question, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask, task_tokens, output_all_attention_masks=True
     height, width = img.shape[0], img.shape[1]
     logits = torch.max(vil_prediction, 1)[1].data  # argmax
      # Load VQA label to answers:
     label2ans_path = os.path.join('save', "VQA" ,"cache", "trainval_label2ans.pkl")
     vqa_label2ans = cPickle.load(open(label2ans_path, "rb"))
     answer = vqa_label2ans[logits[0].item()]
     print("VQA: " + answer)
     # Load GQA label to answers:
     label2ans_path = os.path.join('save', "gqa" ,"cache", "trainval_label2ans.pkl")
     logtis_gqa = torch.max(vil_prediction_gqa, 1)[1].data
     gqa_label2ans = cPickle.load(open(label2ans_path, "rb"))
     answer = gqa_label2ans[logtis_gqa[0].item()]
     print("GQA: " + answer)
     # vil_binary_prediction NLVR2, 0: False 1: True Task 12
     logtis_binary = torch.max(vil_binary_prediction, 1)[1].data
     print("NLVR: " + str(logtis_binary.item()))
     # vil_entaliment:  
     label_map = {0:"contradiction", 1:"neutral", 2:"entailment"}
     logtis_tri = torch.max(vil_tri_prediction, 1)[1].data
     print("Entaliment: " + str(label_map[logtis_tri.item()]))
     # vil_logit: 
     logits_vil = vil_logit[0].item()
     print("ViL_logit: %f" %logits_vil)
     # grounding: 
     logits_vision = torch.max(vision_logit, 1)[1].data
     grounding_val, grounding_idx = torch.sort(vision_logit.view(-1), 0, True)
     examples_per_row = 5
     ncols = examples_per_row 
     nrows = 1
     figsize = [12, ncols*20]     # figure size, inches
     fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)
   # Define bounding boxes and images’ configurations for the output
   for i, axi in enumerate(ax.flat):
         idx = grounding_idx[i]
         val = grounding_val[i]
         box = spatials[0][idx][:4].tolist()
         y1 = int(box[1] * height)
         y2 = int(box[3] * height)
         x1 = int(box[0] * width)
         x2 = int(box[2] * width)
         patch = img[y1:y2,x1:x2]
         axi.set_title(str(i) + ": " + str(val.item()))
 #Make predictions for the text segments
 def custom_prediction(query, task, features, infos):
     tokens = tokenizer.encode(query)
     tokens = tokenizer.add_special_tokens_single_sentence(tokens)
     segment_ids = [0] * len(tokens)
     input_mask = [1] * len(tokens)
     max_length = 37
     if len(tokens) < max_length:
          # Note here we pad in front of the sentence
         padding = [0] * (max_length - len(tokens))
         tokens = tokens + padding
         input_mask += padding
         segment_ids += padding
     text = torch.from_numpy(np.array(tokens)).cuda().unsqueeze(0)
     input_mask = torch.from_numpy(np.array(input_mask)).cuda().unsqueeze(0)
     segment_ids = torch.from_numpy(np.array(segment_ids)).cuda().unsqueeze(0)
     task = torch.from_numpy(np.array(task)).cuda().unsqueeze(0)
     num_image = len(infos)
     feature_list = []
     image_location_list = []
     image_mask_list = []
     for i in range(num_image):
         image_w = infos[i]['image_width']
         image_h = infos[i]['image_height']
         feature = features[i]
         num_boxes = feature.shape[0]
         g_feat = torch.sum(feature, dim=0) / num_boxes
         num_boxes = num_boxes + 1
         feature =[g_feat.view(1,-1), feature], dim=0)
         boxes = infos[i]['bbox']
         image_location = np.zeros((boxes.shape[0], 5), dtype=np.float32)
         image_location[:,:4] = boxes
         image_location[:,4] = (image_location[:,3] - image_location[:,1]) * (image_location[:,2] - image_location[:,0]) / (float(image_w) * float(image_h))
         image_location[:,0] = image_location[:,0] / float(image_w)
         image_location[:,1] = image_location[:,1] / float(image_h)
         image_location[:,2] = image_location[:,2] / float(image_w)
         image_location[:,3] = image_location[:,3] / float(image_h)
         g_location = np.array([0,0,1,1,1])
         image_location = np.concatenate([np.expand_dims(g_location, axis=0), image_location], axis=0)
         image_mask = [1] * (int(num_boxes))
     features = torch.stack(feature_list, dim=0).float().cuda()
     spatials = torch.stack(image_location_list, dim=0).float().cuda()
     image_mask = torch.stack(image_mask_list, dim=0).byte().cuda()
     co_attention_mask = torch.zeros((num_image, num_boxes, max_length)).cuda()
   prediction(text, features, spatials, segment_ids, input_mask, image_mask, co_attention_mask, task) 

13) ViLBERT part

 #Instantiate the FeatureExtractor() class defined above
 feature_extractor = FeatureExtractor()
 #arguments for the ViLBERT model
 args = SimpleNamespace(from_pretrained= "save/multitask_model/pytorch_model_9.bin",
 #Set model configuration path
 config = BertConfig.from_json_file(args.config_file)
 with open('./vilbert_tasks.yml', 'r') as f:
     task_cfg = edict(yaml.safe_load(f))
 #V&L tasks to be performed
 task_names = [
 for i, task_id in enumerate(args.tasks.split('-')):
     task = 'TASK' + task_id
     name = task_cfg[task]['name']
 timeStamp = args.from_pretrained.split('/')[-1] + '-' + args.save_name
 config = BertConfig.from_json_file(args.config_file)
 if args.predict_feature:
     config.v_target_size = 2048
     config.predict_feature = True
     config.v_target_size = 1601
     config.predict_feature = False
 if args.task_specific_tokens:
     config.task_specific_tokens = True    
 if args.dynamic_attention:
     config.dynamic_attention = True
 config.visualization = True
 num_labels = 3129
 if args.baseline:
     model = BaseBertForVLTasks.from_pretrained(
         args.from_pretrained, config=config, num_labels=num_labels, default_gpu=default_gpu
     model = VILBertForVLTasks.from_pretrained(
         args.from_pretrained, config=config, num_labels=num_labels, default_gpu=default_gpu

14)Model evaluation

 cuda = torch.cuda.is_available()
 if cuda: model = model.cuda(0)
 tokenizer = BertTokenizer.from_pretrained(
     args.bert_model, do_lower_case=args.do_lower_case

15)Test the model

 image_path = 'IMAGE_PATH'
 features, infos = feature_extractor.extract_features(image_path)
 img ='RGB')
 img = torch.tensor(np.array(img))
 #Plot the output using matplotlib
 query = "swimming elephant"
 task = [9]
 #pass the inputs to make the custom prediction
 custom_prediction(query, task, features, infos) 

Input image:


Source: GitHub

Find the Google colab notebook of above implementation here.


To have a detailed understanding about the 12-in-1 multitasking model, refer to the following sources:

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