Unhashable type : dict for visualbert example code.
Hi,
I am using the visualbert model as shown in [visualbert visualreasoning](https://huggingface.co/transformers/model_doc/visual_bert.html#visualbertforvisualreasoning)
```
# Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
from transformers import BertTokenizer, VisualBertForVisualReasoning
import torch

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = VisualBertForVisualReasoning.from_pretrained('uclanlp/visualbert-nlvr2')

text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors='pt')
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) #example
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)

inputs.update({{
    "visual_embeds": visual_embeds,
    "visual_token_type_ids": visual_token_type_ids,
    "visual_attention_mask": visual_attention_mask
}})

labels = torch.tensor(1).unsqueeze(0)  # Batch size 1, Num choices 2

outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
```
 and I encountered the following error:
```
Traceback (most recent call last):

  File "<ipython-input-1-8716adc0686f>", line 1, in <module>
    runfile('/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments/example.py', wdir='/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments')

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments/example.py", line 219, in <module>
    "visual_attention_mask": visual_attention_mask

TypeError: unhashable type: 'dict'
```

Is this operation supported by python or is this a bug in the code? 
Transformers-cli env output:
```

- `transformers` version: 4.9.2
- Platform: Darwin-16.7.0-x86_64-i386-64bit
- Python version: 3.6.13
- PyTorch version (GPU?): 1.9.0 (False)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
```

@patil-suraj 
You have 2 {{ in your code, whereas it should be only one:

```
inputs.update({
    "visual_embeds": visual_embeds,
    "visual_token_type_ids": visual_token_type_ids,
    "visual_attention_mask": visual_attention_mask
})
```<|||||>yes I guessed that, i.e, it used sets instead of dicts. But then it should be modified in the documentation as well. Also, doing as you said threw another error

```
Traceback (most recent call last):

  File "<ipython-input-2-8716adc0686f>", line 1, in <module>
    runfile('/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments/example.py', wdir='/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments')

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments/example.py", line 224, in <module>
    outputs = model(**inputs, labels=labels)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/transformers/models/visual_bert/modeling_visual_bert.py", line 1240, in forward
    return_dict=return_dict,

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/transformers/models/visual_bert/modeling_visual_bert.py", line 784, in forward
    visual_input_shape = visual_embeds.size()[:-1]

TypeError: 'int' object is not callable
```
@NielsRogge <|||||>Tagging @gchhablani as he's the expert on VisualBERT<|||||>Thanks for the tag @NielsRogge.

@abhijithneilabraham There was an error in the docs earlier. The dictionary update is wrong. It should not have `{{` and `}}`, but `{` and `}` instead. It was fixed recently in a PR. Sorry about that.

Please let me know if this solves your issue.<|||||>> Please let me know if this solves your issue.

Apparently that doesn't solve his issue, as he shows above.<|||||>@gchhablani I would like to help with the issue if I can. Let me know.<|||||>@abhijithneilabraham Can you share your `get_visual_embeddings` method if possible?<|||||>@gchhablani I used it from the [colab notebook](https://colab.research.google.com/drive/1bLGxKdldwqnMVA5x4neY7-l_8fKGWQYI?usp=sharing) that you shared in the doc. I still was unclear on the proper way of using it.<|||||>Also @gchhablani  this was the issue encountered after modifying the example code like the way you mentioned.

```
Traceback (most recent call last):

  File "<ipython-input-2-8716adc0686f>", line 1, in <module>
    runfile('/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments/example.py', wdir='/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments')

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "/Users/abhijithneilabraham/Documents/GitHub/visualbert_experiments/example.py", line 224, in <module>
    outputs = model(**inputs, labels=labels)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/transformers/models/visual_bert/modeling_visual_bert.py", line 1240, in forward
    return_dict=return_dict,

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)

  File "/Users/abhijithneilabraham/miniconda3/envs/py/lib/python3.6/site-packages/transformers/models/visual_bert/modeling_visual_bert.py", line 784, in forward
    visual_input_shape = visual_embeds.size()[:-1]

TypeError: 'int' object is not callable
```

Could this be because of the improper way of using the visual embeds? If yes I'd like to understand a proper approach to generating the visual embeds with a function<|||||>@gchhablani  This is my source code

```
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 16 11:22:20 2021

@author: abhijithneilabraham
"""

import torch,torchvision
import matplotlib.pyplot as plt
import json
import cv2
import numpy as np

from detectron2.modeling import build_model
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.structures.image_list import ImageList
from detectron2.data import transforms as T
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputs
from detectron2.structures.boxes import Boxes
from detectron2.layers import nms
from detectron2 import model_zoo
from detectron2.config import get_cfg

img1 = plt.imread(f'profile_pic.jpeg')

# Detectron expects BGR images
img_bgr1 = cv2.cvtColor(img1, cv2.COLOR_RGB2BGR)

cfg_path = "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml"

def load_config_and_model_weights(cfg_path):
    cfg = get_cfg()
    cfg.merge_from_file(model_zoo.get_config_file(cfg_path))

    # ROI HEADS SCORE THRESHOLD
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5

    # Comment the next line if you're using 'cuda'
    cfg['MODEL']['DEVICE']='cpu'

    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(cfg_path)

    return cfg

cfg = load_config_and_model_weights(cfg_path)

def get_model(cfg):
    # build model
    model = build_model(cfg)

    # load weights
    checkpointer = DetectionCheckpointer(model)
    checkpointer.load(cfg.MODEL.WEIGHTS)

    # eval mode
    model.eval()
    return model

model = get_model(cfg)

def prepare_image_inputs(cfg, img_list):
    # Resizing the image according to the configuration
    transform_gen = T.ResizeShortestEdge(
                [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST
            )
    img_list = [transform_gen.get_transform(img).apply_image(img) for img in img_list]

    # Convert to C,H,W format
    convert_to_tensor = lambda x: torch.Tensor(x.astype("float32").transpose(2, 0, 1))

    batched_inputs = [{"image":convert_to_tensor(img), "height": img.shape[0], "width": img.shape[1]} for img in img_list]

    # Normalizing the image
    num_channels = len(cfg.MODEL.PIXEL_MEAN)
    pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1)
    pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1)
    normalizer = lambda x: (x - pixel_mean) / pixel_std
    images = [normalizer(x["image"]) for x in batched_inputs]

    # Convert to ImageList
    images =  ImageList.from_tensors(images,model.backbone.size_divisibility)
    
    return images, batched_inputs

images, batched_inputs = prepare_image_inputs(cfg, [img_bgr1])
def get_features(model, images):
    features = model.backbone(images.tensor)
    return features

features = get_features(model, images)



def get_proposals(model, images, features):
    proposals, _ = model.proposal_generator(images, features)
    return proposals

proposals = get_proposals(model, images, features)


def get_box_features(model, features, proposals):
    features_list = [features[f] for f in ['p2', 'p3', 'p4', 'p5']]
    box_features = model.roi_heads.box_pooler(features_list, [x.proposal_boxes for x in proposals])
    box_features = model.roi_heads.box_head.flatten(box_features)
    box_features = model.roi_heads.box_head.fc1(box_features)
    box_features = model.roi_heads.box_head.fc_relu1(box_features)
    box_features = model.roi_heads.box_head.fc2(box_features)

    box_features = box_features.reshape(1, 1000, 1024) # depends on your config and batch size
    return box_features, features_list

box_features, features_list = get_box_features(model, features, proposals)



def get_prediction_logits(model, features_list, proposals):
    cls_features = model.roi_heads.box_pooler(features_list, [x.proposal_boxes for x in proposals])
    cls_features = model.roi_heads.box_head(cls_features)
    pred_class_logits, pred_proposal_deltas = model.roi_heads.box_predictor(cls_features)
    return pred_class_logits, pred_proposal_deltas

pred_class_logits, pred_proposal_deltas = get_prediction_logits(model, features_list, proposals)


def get_box_scores(cfg, pred_class_logits, pred_proposal_deltas):
    box2box_transform = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)
    smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA

    outputs = FastRCNNOutputs(
        box2box_transform,
        pred_class_logits,
        pred_proposal_deltas,
        proposals,
        smooth_l1_beta,
    )

    boxes = outputs.predict_boxes()
    scores = outputs.predict_probs()
    image_shapes = outputs.image_shapes

    return boxes, scores, image_shapes

boxes, scores, image_shapes = get_box_scores(cfg, pred_class_logits, pred_proposal_deltas)




def get_output_boxes(boxes, batched_inputs, image_size):
    proposal_boxes = boxes.reshape(-1, 4)
    scale_x, scale_y = (batched_inputs["width"] / image_size[1], batched_inputs["height"] / image_size[0])
    output_boxes = Boxes(proposal_boxes)
#    output_boxes.scale(scale_x, scale_y)
    output_boxes.clip(image_size)

    return output_boxes

output_boxes = [get_output_boxes(boxes[i], batched_inputs[i], proposals[i].image_size) for i in range(len(proposals))]


def select_boxes(cfg, output_boxes, scores):
    test_score_thresh = cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST
    test_nms_thresh = cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST
    cls_prob = scores.detach()
    cls_boxes = output_boxes.tensor.detach().reshape(1000,80,4)
    max_conf = torch.zeros((cls_boxes.shape[0]))
    for cls_ind in range(0, cls_prob.shape[1]-1):
        cls_scores = cls_prob[:, cls_ind+1]
        det_boxes = cls_boxes[:,cls_ind,:]
        keep = np.array(nms(det_boxes, cls_scores, test_nms_thresh))
        max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep], cls_scores[keep], max_conf[keep])
    keep_boxes = torch.where(max_conf >= test_score_thresh)[0]
    return keep_boxes, max_conf


temp = [select_boxes(cfg, output_boxes[i], scores[i]) for i in range(len(scores))]
keep_boxes, max_conf = [],[]
for keep_box, mx_conf in temp:
    keep_boxes.append(keep_box)
    max_conf.append(mx_conf)
    
    
    
MIN_BOXES=10
MAX_BOXES=100
def filter_boxes(keep_boxes, max_conf, min_boxes, max_boxes):
    if len(keep_boxes) < min_boxes:
        keep_boxes = np.argsort(max_conf).numpy()[::-1][:min_boxes]
    elif len(keep_boxes) > max_boxes:
        keep_boxes = np.argsort(max_conf).numpy()[::-1][:max_boxes]
    return keep_boxes

keep_boxes = [filter_boxes(keep_box, mx_conf, MIN_BOXES, MAX_BOXES) for keep_box, mx_conf in zip(keep_boxes, max_conf)]


def get_visual_embeds(box_features, keep_boxes):
    return box_features[keep_boxes.copy()]

visual_embeds = np.asarray([get_visual_embeds(box_feature, keep_box) for box_feature, keep_box in zip(box_features, keep_boxes)])


# Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
from transformers import BertTokenizer, VisualBertForQuestionAnswering


tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = VisualBertForQuestionAnswering.from_pretrained('uclanlp/visualbert-vqa')

text = "what color dress is he wearing?"
inputs = tokenizer(text, return_tensors='pt')

visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) #example
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)

inputs.update({
    "visual_embeds": visual_embeds,
    "visual_token_type_ids": visual_token_type_ids,
    "visual_attention_mask": visual_attention_mask
})

labels = torch.tensor([[0.0,1.0]]).unsqueeze(0)  # Batch size 1, Num labels 2

outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
print(outputs)
```<|||||>@abhijithneilabraham  The issue is that you are using a numpy array when `visual_embeds` expects a torch tensor:

```python
>>> import numpy as np
>>> import torch
>>> a = np.ones(10)
>>> a.size
10
>>> b = torch.ones(10)
>>> b.size
<built-in method size of Tensor object at 0x7fb34109ebc0>
>>> b.size()
torch.Size([10])
```

I believe you can check the other demo, where the LXMERT authors have provided FasterRCNN classes and the pre-trained model on Visual Genome. It'll be much easier to use that.

EDIT
------
The docs issue has been fixed, the docs have not yet updated, I guess.
<|||||>Much thanks @gchhablani ! Can you share the link to the other demo? I can then close this issue.<|||||>@abhijithneilabraham No problem :)


Here is the demo link : https://github.com/huggingface/transformers/tree/master/examples/research_projects/visual_bert<|||||>Thank you! 