vqassessment / explainablevqa Goto Github PK

Hi,
The idea of separate different type of feature is very delicate. I just pulled this project and during running of this demo 'python evaluate_one_video.py -v ./demo/17734.mp4 -f', there was an import error said 'File "/group/dphi_dmz/tianhaos/VQA/DOVER/dover/models/conv_backbone.py", line 7, in
from open_clip import CLIP3D
ImportError: cannot import name 'CLIP3D' from 'open_clip' (/group/dphi_dmz/tianhaos/VQA/open_clip/src/open_clip/init.py)'. Could you guys check if there is a name typo or open_clip version problem? THX

在下面这段代码里，我不太理解loss1是做什么的, 可以请您解释一下嘛？

def plcc_loss(y_pred, y):
    sigma_hat, m_hat = torch.std_mean(y_pred, unbiased=False)
    y_pred = (y_pred - m_hat) / (sigma_hat + 1e-8)
    sigma, m = torch.std_mean(y, unbiased=False)
    y = (y - m) / (sigma + 1e-8)
    loss0 = torch.nn.functional.mse_loss(y_pred, y) / 4
    rho = torch.mean(y_pred * y)
    loss1 = torch.nn.functional.mse_loss(rho * y_pred, y) / 4
    return ((loss0 + loss1) / 2).float()

Could you please provide the MaxWell_val.csv file? Thank you very much :)

Hi, I have a question about (Maxwell_train and val dataset) these 2 csv files, is the order of these 2 files corresponding to the sequence of the download video sequence? Thank you in advance:)

Hi, When I run the demo_maxvqa.py for a test, something is wrong with the shape:

Traceback (most recent call last):
File "E:/MaxVQA-master/demo_maxvqa.py", line 167, in
a = inference(video)
File "E:/MaxVQA-master/demo_maxvqa.py", line 160, in inference
vis_feats = visual_encoder(data["aesthetic"].to(device), data["technical"].to(device))
File "D:\tools\Anaconda\set\envs\python37tf\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "E:\MaxVQA-master\model\visual.py", line 19, in forward
clip_feats = clip_feats[1:].reshape(7,7,-1,1024).permute(3,2,0,1)
RuntimeError: shape '[7, 7, -1, 1024]' is invalid for input of size 64512

vis_feats = visual_encoder(data["aesthetic"].to(device), data["technical"].to(device))
data["aesthetic"]---[3, 64, 224, 224]
data["technical"]---[3, 128, 224, 224]

The specific problem is found in the following two lines of code
clip_feats = self.clip_visual(x_aes)
clip_feats = clip_feats[1:].reshape(7,7,-1,1024).permute(3,2,0,1)
However, the shape of clip_feats is [64, 1024]

Hi,

Thank you very much for sharing the interesting work and I really enjoyed reading your paper!

Could you please elaborate on how you produced the local quality maps from the final features in Figure 7? In particular how is the map for each dimension (e.g. sharpness) generated from $f^{Final}_{V_t}$?

Thank you in advance for your help.

Thank you very much for sharing the interesting work and I really enjoyed reading your paper!
When will the relevant data of the dataset Maxwell be released?