hotaekhan / sstdnet Goto Github PK

Hi, I have a question about code in loss.py. Why do you exclude the background, and only use the object labels, when computing focal loss?

Hi, HotaekHan, thanks for sharing the code.

I have a question concerning the details of SSTD net, and I'm really looking forward to see you reply:)

(1) In the deconvolution part, I see that you use groups=64 to upsample. But generally groups=1 might be more reasonale, so I guess it's for saving computational complexity? Or is there any other reasons?

(2) The original paper uses deconv33, conv11 to eastablish attention map. I see that you're using deconv1616 and two conv33 to do it. Does it mean that this implementation is better than that in the original paper?

It's a very nice code and I really appretite your comment!

Thanks

How to prepare training data? After I run python3 datagen.py, errors happens

Traceback (most recent call last):
  File "datagen.py", line 540, in <module>
    test()
  File "datagen.py", line 531, in test
    for images, loc_targets, cls_targets, mask_targets in dataloader:
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 310, in __iter__
    return DataLoaderIter(self)
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 180, in __init__
    self._put_indices()
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 219, in _put_indices
    indices = next(self.sample_iter, None)
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/sampler.py", line 119, in __iter__
    for idx in self.sampler:
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/sampler.py", line 50, in __iter__
    return iter(torch.randperm(len(self.data_source)).long())
RuntimeError: invalid argument 1: must be strictly positive at /pytorch/torch/lib/TH/generic/THTensorMath.c:2184

Thanks!

so nice to share the code here.
I have a question, the text bounding box may be incline in one image. so to determine a inline bounding box, (xmin, ymin, xmax, ymax) is not enough, for example, we may need three points to determine a bounding box. why here, you only use (xmin, ymin, xmax, ymax) for training labels?
thank!

thanks

I tested your code with image size 512, and is take a lot of time to decode.

Elapsed time of pred : 91.725ms
Decoding..
Elapsed time of decode : 114360.36300000001ms
Avg. elapsed time of pred : 153.09623809523805ms
Avg. elapsed time of decode : 65703.0309047619ms

I learned that NSM function will run slowly in image with many objects. How can i improve its performance.

Hi, I've downloaded a public dataset with annotation, and I've followed the instructions on README, but i'm not sure whether I can just proceed like that.
I see there is a resize function on datagen.py, does it mean I can include image with different sizes/rectangular image? Also, if there is a resize function, will the annotation be affected? Should I change it to relative value instead?

Thanks in advance!

Traceback (most recent call last):
File "train.py", line 192, in
train(epoch)
File "train.py", line 133, in train
loss = ((loc_loss + cls_loss) / num_matched_anchors) + mask_loss
RuntimeError: invalid argument 3: divide by zero at /pytorch/torch/lib/THC/generic/THCTensorMathPairwise.cu:88

The error occurs while training the model...how should i solve it?

Epoch: 0
Traceback (most recent call last):
File "train.py", line 194, in
train(epoch)
File "train.py", line 118, in train
for batch_idx, (inputs, loc_targets, cls_targets, mask_targets) in enumerate(trainloader):
File "/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.py", line 336, in next
return self._process_next_batch(batch)
File "/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.py", line 357, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
RuntimeError: Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.py", line 106, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/xendity/SSTDNet/datagen.py", line 492, in collate_fn
loc_target, cls_target = self.data_encoder.encode(boxes[i], labels[i], input_size=(max_w,max_h))
File "/home/xendity/SSTDNet/encoder.py", line 92, in encode
anchor_boxes = self._get_anchor_boxes(input_size)
File "/home/xendity/SSTDNet/encoder.py", line 66, in _get_anchor_boxes
xy = (xy*grid_size).view(fm_h,fm_w,1,2).expand(fm_h,fm_w,9,2)
RuntimeError: Expected object of type torch.LongTensor but found type torch.FloatTensor for argument #2 'other'

Hi, I ran train.py and got two or three type error like this. How should I modify the code?

I have a question about anchor_areas， the anchor_areas in encoder.py of your code is [1616., 3232., ..., 256*256.], and I want to know the reason you set them. I think they are correlated with feature maps, but I can't get the explicit relation.

The ori-paper works for text detection，but why this repo say “This code is work for general object detection problem. not for (oriented) text detection problem”？