https://hypothesis.readthedocs.io/en/latest/ works pretty well for finding corner cases.

I propose to extend the existing unit test functionality with the tests based on the Hypothesis library.

Traceback (most recent call last):
File "main.py", line 103, in
tf.app.run()
File "C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\platform\app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "main.py", line 81, in main
data_dir=FLAGS.data_dir)
File "D:\DCGAN-tensorflow-master\model.py", lin

We trained the ms-coco dog set as per the tutorial under two-days to a demo.
Training process completes successfully..but after testing with a sample image, there is no bounding box detected..similar thing happens with custom dataset.
We are using the docker image with tag 19.09-caffe

PLEASE FOLLOW THESE INSTRUCTIONS BEFORE POSTING

1. Read the README.md thoroughly ! README.md is not a decoration.
2. Please search existing open and closed issues in case your issue has already been reported
3. Please try to debug the issue in case you can solve it on your own before posting

After following steps above and agreeing to provide the detailed information requested be

In file binary segmentation (camvid).ipynb, block 5, there is:

# Lets look at data we have
dataset = Dataset(x_train_dir, y_train_dir, classes=['car', 'pedestrian'])
image, mask = dataset[5] # get some sample
visualize(
    image=image, 
    cars_mask=mask[..., 0].squeeze(),
    sky_mask=mask[..., 1].squeeze(),
    background_mask=mask[..., 2].squeeze(),
)

here, sky_mask

best triplet loss ever

https://github.com/adambielski/siamese-triplet
https://github.com/andreasveit/triplet-network-pytorch
https://github.com/CoinCheung/triplet-reid-pytorch
https://discuss.pytorch.org/t/triplet-loss-in-pytorch/30634

Currently in rs feature we extract polygons and simplify them. Then in rs merge we buffer, then union, then unbuffer for merging shapes across tile boundaries. This leads to polygons that are no longer simplified.

We should investigate not simplifying in rs feature. Instead merge un

How do you normalize the point sets to be zero mean and within a unit ball?
I use the following method, but it seems to be wrong.
`
void normalized()
{
ifstream inputfile;
string filename;
string obj_root_path = "./DataSet/ModelNet40_obj";
string ply_root_path = "./DataSet/ModelNet40_Ply_Normalized";
string filename2 = "filenames.txt";

inputfile.open(filename2);
while

I am not clear about what the iteration means. Could you explain more about it? Also, how is it related to epoch?

Thanks.

I want to extend your get_deconv_filter from 2D to 3D. In CAFFE, it can be implemented as

int f = ceil(blob->shape(-1) / 2.);
    float c = (2 * f - 1 - f % 2) / (2. * f);
    for (int i = 0; i < blob->count(); ++i) {
      float x = i % blob->shape(-1);
      float y = (i / blob->shape(-1)) % blob->shape(-2);
      float z = (i/(blob->shape(-1)*blob->shape(-2))) % blob->shape(-3);