Always predict to the same Class

[SampsonKwan]

@qijiezhao I tried your code and the pretrain mode on Kinectics, but I always got strange results: even though I fed different videos (in different class) to the model, most of them were predicted to the same label. I fist thought there was something wrong with my preprocessing code, but when I tried to feed it with random data, the result is the same:

if __name__ == '__main__':
    model = P3D199(pretrained=True, num_classes=400)
    model = model.cuda()
    model.eval()

    batch_num = 1000  # test 1000 batches(randomly)
    cnt_M = np.zeros(400)
    for j in range(batch_num):
        data = torch.autograd.Variable(
            torch.rand(6, 3, 16, 160, 160)).cuda()  # if modality=='Flow', please change the 2nd dimension 3==>2
        out = model(data)
        out = torch.nn.functional.softmax(out, dim=1)
        vid_result = out.data.cpu().numpy()
        score = np.mean(vid_result, axis=0) # take the average score of this 6 clips as the video-score
        max_scr = np.max(score)
        max_ind = np.argmax(score)
        cnt_M[max_ind] += 1

    cnt_max = np.max(cnt_M)
    ind_max = np.argmax(cnt_M)
    print(cnt_M)
    print("most_predict_index: " + str(ind_max) + "    count/all: " + "%d / %d" % (
        cnt_max, batch_num))

and my result is :

load pretrained model success.
[    0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.  1000.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.]
most_predict_index: 291    count/all: 1000 / 1000

I tried this for many time, they were always predicted to the same label. Is there some problem with the pre-trained model? Thx

[qijiezhao]

Read the updated README.md. I have analyzed the problem that you mentioned. and support two solutions.