mplug中两处代码错误问题

AliceMind/mPLUG
/caption_mplug.py中234-239行中，无论是visual encoder用了ViT-B-16还是ViT-L-14，在根据输入图像分辨率更改positional embedding时，创建的pos_embedd的dim=1都是768。
```
# reshape positional embedding to accomodate for image resolution change
if config["clip_name"] == "ViT-B-16":
num_patches = int(config["image_res"] * config["image_res"]/(16*16))
elif config["clip_name"] == "ViT-L-14":
num_patches = int(config["image_res"] * config["image_res"]/(14*14))
pos_embed = nn.Parameter(torch.zeros(num_patches + 1, 768).float())

pos_embed = resize_pos_embed(state_dict['visual_encoder.visual.positional_embedding'].unsqueeze(0),
pos_embed.unsqueeze(0))
state_dict['visual_encoder.visual.positional_embedding'] = pos_embed
```
而在初始化MPLUG模型中初始化CLIP的时，即调用AliceMind/models
/visual_transformers.py中初始化CLIP代码时，positional embedding的dim=1的值时根据visual encoder用了ViT-B-16还是ViT-L-14而决定的，B-16时dim=1为768，L-14时dim=1为1024。
```
def initialize_clip(config, num_patches=240):
from models.clip import clip
if config["clip_name"] == "ViT-B-16":
clip_model, preprocess = clip.load("ViT-B-16.tar", jit=False)
num_patches = int(config['image_res']*config['image_res']/(16*16))
pos_embed = nn.Parameter(torch.zeros(num_patches + 1, 768).float())
elif config["clip_name"] == "ViT-L-14":
clip_model, preprocess = clip.load("ViT-L-14.tar", jit=False)
num_patches = int(config['image_res']*config['image_res']/(14*14))
pos_embed = nn.Parameter(torch.zeros(num_patches + 1, 1024).float())
pos_embed.weight = resize_pos_embed(clip_model.visual.positional_embedding.unsqueeze(0), pos_embed.unsqueeze(0))
clip_model.visual.positional_embedding = pos_embed
return clip_model, preprocess
```
两代码的区别造成
`RuntimeError: Error(s) in loading state_dict for MPLUG: size mismatch for visual_encoder.visual.positional_embedding: copying a param with shape torch.Size([x, 768]) from checkpoint, the shape in current model is torch.Size([x, 1024]).`的错误。

如有错误敬请指正

Compare the positional-embedding initialization in caption_mplug.py lines 234-239 with initialize_clip in models/visual_transformers.py for both ViT-B-16 and ViT-L-14. Reproduce the reported state_dict loading error and verify that the embedding dimensions used for resizing and model loading are consistent for each visual encoder.

Written by the indexing model from the issue text.