Link expires？https://projects4jw.blob.core.windows.net/model/focal-transformer/imagenet1k/focal-base-useconv-is224-ws7.pth Can you reupload this model？

I was confused by the relationship between focal window size and focal region size. Can you explained it more clearly? Take stage 1 level 0 as an example, sw=1, sr = 13, sw*sr could not be divided by the Ouput Size 56. I could not understand why sr is 13. Thanks a lot if you could help me.

In your code, I do not understand how to get q, k, v from x and x_pooled. I have been confused by the roll operation on k_windows and the unfold operation on k_pooled_k for several days. Take stage 1 as an example, for level 0, since the sw is 1, I think the sr should be window_size//sw, that is 7. And for level 1, since the sw is 7, I think the sr should be output_size//sw, that is 8. Therefore the number of k is 7*7+8*8 = 113. But in your paper, you set sr as 13 at lavel 0 , sr as 7 at level 1. Why 13 and 7? And in your code, the number of k is 7*7+4*7*7-4*(7-3)*(7-3)+7*7=230, which is different from 7*7+13*13=218. As a suggestion, the window attention should be writen more clearly, and more comments are need in your code. Thanks a lot. If there is something wrong with what I said, please forgive me.

hi,I'm sorry to bother you . I would like to know whether the num_heads values in each of the four stages in your code are the same, or whether the num_heads values in each stage are fixed or related to something else

Hi,I want to know doing sub-windows pooling，If I want to follow the sub-window-pooling method introduced in the paper, which pooling method should I select.

thank you very much

Great Work !

My query is that : can this method be run on 1-D features ? Because the pooling operations done before flattening is done for 2-D representation !

hi,I'm sorry to bother you . I would like to know whether the num_heads values in each of the four stages in your code are the same, or whether the num_heads values in each stage are fixed or related to something else

https://projects4jw.blob.core.windows.net/model/focal-transformer/imagenet1k/focal-tiny-is224-ws7.pth ResourceNotFound The specified resource does not exist. RequestId:933ca8ee-601e-0028-4af4-f455bc000000 Time:2022-11-10T11:05:52.4511547Z

Thanks for your great work! But I don't understand the meaning of the following code (class FocalTransformerBlock in ./classification/focal_transformer.py) at Link

            for k in range(self.focal_level-1):     
                window_size_glo = math.floor(self.window_size_glo / (2 ** k))
                pooled_h = math.ceil(H / self.window_size) * (2 ** k)
                pooled_w = math.ceil(W / self.window_size) * (2 ** k)
                H_pool = pooled_h * window_size_glo
                W_pool = pooled_w * window_size_glo

I guss the purpose of this is to make the H and W of x_level_k a multiple of window size and to facilitate the pool operation. But how does this actually work?

• Why calculate window_size_glo?
• What is meaning of pooled_h ? (math.ceil(H / self.window_size) does not change over iteration)
• Why not change window_size over iteration directly?

Could you please explain to me in details? Thanks in advance.

Hello, should the range_h and range_w on line 176 and line 177 of the file (https://github.com/microsoft/Focal-Transformer/blob/main/classification/focal_transformer_v2.py) be different at different focal levels? The same size range_h and range_w are used at different focal levels in the code; this makes me very puzzled.

The positional embedding is related to the input size, is it possible to load the pretrained models on classification? By the way, it seems that torch.unfold is faster than torch.roll on GPUs.

Hello, first of all, thank you for providing the Focal Transformer module,

I have some questions:

1. The image you are processing is 224224 and window size = 7. I wonder if it is reasonable for me to change the window size to 8 in order to make it divisible when I input 512512 at the beginning.

2. Since you didn't give the segmentation code, IT seems to me that you set focal level as 2 in your demo. Should I set level values as 1, 2 and 3 respectively when processing? To find the best fit.

3. As for num_heads, I see that you are patch_embeding, and the number of channels becomes 96. Then in focal attention,num_heads = 2 in order to divide evenly.

4. Suppose that the size of the image I input is 3232, since patch_size = 4, the length of the vector is 64, then the size I input should increase successively, such as 6464, 128*128. Should I also increase ptach_size so that the final vector length is still 64?

If you can help me, I will be very grateful to you