Hi, thanks for generously sharing your code. When I try to extract optical flow features of Kinetic using BNInception, I encountered some problems.

• I don't know the data preprocessing method for BNInception. Could you please provide a more complete code?
• I notice there are some configurations to extract optical flow using denseflow. Which configuration of denseflow did you use? Such as: denseflow test.avi -b=20 -a=tvl1 -s=1 -v?

Hi,

I have tried the code and get 68.3% on thumos dataset, which is slightly lower than the result on paper (69.5%)

Can you provide one or two features and label file so that I can compare with and have sanity check?

Hi, thank you for sharing the code, I met some problems when I prepare the dataset.

1. I want to know how you extract the feature of TVSeries？ Is the input image the original size? Did you do any crop or resize?

2. I get the target_perframe.npy on the frame-level. For example, if one video time is 200 seconds, FPS is 4, the frame of the video is 800, I get the annotation by .txt and transfer the seconds in the annotaion to frames (by multiply the FPS), and for each frame, if any action happens in this frame,this action corresponds to the value in the vector will plus one. Finally I will get a 800 x number of classes's array. Is there any mistake in my operation?

3. Would you please share the training log so thao I can compare it with my log?

Thank you to share your great work!

I have something to ask you.

I have my own dataset that I want to apply your LSTR to, and for this I did feature extraction for rgb and flow respectively according to this https://github.com/open-mmlab/mmaction2/tree/master/tools/data/activitynet. However, the feature results came out in pkl format. To use your dataloader, it must be in npy format. How can I get the features out in npy format like you?

Hi,

I'm a beginner of video understanding and OAD. I have installed mmaction2 and denseflow, it works well. But I'm not sure how to extract perframe features using it.

1. Is there any ready-made apis to solve the above problem? I've taken much time to search the solutions, but I didn't find.
2. If no ready-made apis, could you plz share related codes with us? It's important for beginners.

Thank you for your awesome repo. And hope your reply.

@xumingze0308 Hi Xu,

I followed the url https://github.com/yjxiong/action-detection/blob/master/transforms.py that you mentionded in other issues. But I found that this data preprocessing is so inefficient, that is, processing one .jpg files frame by frame. And it's easy to cause memory leak problems by using the function PIL.Image.open(). I don't know where I am going wrong, my code process is as follows.

def transforms_img(img_list):

    trans = torchvision.transforms.Compose([
        GroupScale(256),
        GroupRandomCrop(224),
        Stack(),
        ToTorchFormatTensor(),
        GroupNormalize(
            mean=[128],
            std=[128]
        )]
    )

    for i, img_dir in enumerate(img_list):
        with open(img_dir, 'rb') as open_file:
            img = Image.open(open_file).convert('L')
            color_group = [img]
            rst = trans(color_group)
            del color_group
            del img
        if i == 0:
            stack_img = rst
            del rst
        else:
            stack_img = torch.cat((stack_img, rst), dim=0)
            del rst
    gc.collect() 

Hi, Many factors can affect the inference speed of the model. Such as the evaluation method(batch mode, stream mode) of the LSTR. Could you please provide more details, such as the batch_size? It would be better if you could provide the speed testing code.

Hi,

Thanks for the awesome code repo. I observed that there is a large imbalance in dataset. For TVSeries, >70% of data is background class.

1. I was wondering if you tried any technique to deal with the imbalance in the dataset? The LSTR paper does not mention any such technique.

2. My validation loss does not converge and most predictions are of background class. My mcAP does not increase over epochs. What metric do you recommend to track/debug over epochs for performance of non-background classes on TVSeries?

Thanks!

I want some assistance with regard to the data preparation and execution? I have downloaded the required files but got some errors in the execution. A rapid response would be greatly appreciated. Thanks

Hi. Thanks for sharing the codebase of LSTR. I was unable to get hold of this Kinetics-pretrained RN50 checkpoint which you have mentioned in your readme. It would be quite helpful if you could share this pretrained backbone (and if possible, for ActivityNet too).

Thanks.

Sorry to bother you.I notice the decreasing of test_loss and the mAP are not synchronized ideally with the epoch increasing.So I’m not sure when stopping the training, and how calculating the mAP in fig3?