Issue in M-Bert-Base-ViT-B clip head linear layer size

[shreyajain4]

I tried the following piece of code present in the repo at location https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/src/multilingual_clip.py

The only changes I made is that I added print statements in between.

---

` import pickle

import torch import transformers

AVAILABLE_MODELS = { 'M-BERT-Distil-40': { 'model_name': 'M-CLIP/M-BERT-Distil-40', 'tokenizer_name': 'M-CLIP/M-BERT-Distil-40', 'head_name': 'M-BERT Distil 40 Linear Weights.pkl' },

'M-BERT-Base-69': {
    'model_name': 'M-CLIP/M-BERT-Base-69',
    'tokenizer_name': 'M-CLIP/M-BERT-Base-69',
    'head_name': 'M-BERT-Base-69 Linear Weights.pkl'
},

'Swe-CLIP-500k': {
    'model_name': 'M-CLIP/Swedish-500k',
    'tokenizer_name': 'M-CLIP/Swedish-500k',
    'head_name': 'Swedish-500k Linear Weights.pkl'
},

'Swe-CLIP-2M': {
    'model_name': 'M-CLIP/Swedish-2M',
    'tokenizer_name': 'M-CLIP/Swedish-2M',
    'head_name': 'Swedish-2M Linear Weights.pkl'
},

'M-BERT-Base-ViT-B': {
    'model_name': 'M-CLIP/M-BERT-Base-ViT-B',
    'tokenizer_name': 'M-CLIP/M-BERT-Base-ViT-B',
    'head_name': 'M-BERT-Base-69-ViT Linear Weights.pkl'
},

}

class MultilingualClip2(torch.nn.Module): def init(self, model_name, tokenizer_name, head_name, weights_dir='data/weights/'): super().init() self.model_name = model_name self.tokenizer_name = tokenizer_name self.head_path = weights_dir + head_name

    self.tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_name)
    self.transformer = transformers.AutoModel.from_pretrained(model_name)
    self.clip_head = torch.nn.Linear(in_features=768, out_features=640)
    self._load_head()

def forward(self, txt):
    txt_tok = self.tokenizer(txt, padding=True, return_tensors='pt').to(device)
    embs = self.transformer(**txt_tok)[0]
    print('embs_text')
    print(embs.size())
    att = txt_tok['attention_mask']
    print('att_text')
    print(att.size())
    embs = (embs * att.unsqueeze(2)).sum(dim=1) / att.sum(dim=1)[:, None]
    print('embs_text')
    print(embs.size())
    p =  self.clip_head(embs)
    print('clip head obj')
    print(self.clip_head)
    print('cliphed_text')
    print(p.size())
    return p

def _load_head(self):
    with open(self.head_path, 'rb') as f:
        lin_weights = pickle.loads(f.read())
    self.clip_head.weight = torch.nn.Parameter(torch.tensor(lin_weights[0]).float().t())
    self.clip_head.bias = torch.nn.Parameter(torch.tensor(lin_weights[1]).float())
    print('ok')
    print(self.clip_head.weight.size())
    print(self.clip_head.bias.size())

def load_model2(name): config = AVAILABLE_MODELS[name] return MultilingualClip2(**config)

mod = load_model2('M-BERT-Base-ViT-B') z = mod(Query[0]) `

Output for this code : ok torch.Size([512, 768]) torch.Size([512]) embs_text torch.Size([1, 6, 768]) att_text torch.Size([1, 6]) embs_text torch.Size([1, 768]) clip head obj Linear(in_features=768, out_features=640, bias=True) cliphed_text torch.Size([1, 512])

---

This output suggest that the file 'M-BERT-Base-69-ViT Linear Weights.pkl' doesn't have the size of 640 X 768 but 512 X 768

Is there any issue with the config then ?

[shreyajain4]

@FreddeFrallan please have a look

[ozmig77]

Hello, I looked into this issue too.

I think the issue is related to CLIP embedding size. Where 512 in ViT, 640 in ResNet. Since M-BERT-Base-69-ViT use CLIP ViT, the 512 seems right.

However I think out_features should be included in configuration for prevent misunderstanding.