[GitHub] [incubator-mxnet] arcadiaphy commented on issue #14058: add backgroud class in box_nms

2019-03-10 Thread GitBox 
arcadiaphy commented on issue #14058: add backgroud class in box_nms
URL: https://github.com/apache/incubator-mxnet/pull/14058#issuecomment-471387717
 
 
   @zhreshold OK to go now.


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[GitHub] [incubator-mxnet] arcadiaphy commented on issue #14058: add backgroud class in box_nms

2019-03-07 Thread GitBox 
arcadiaphy commented on issue #14058: add backgroud class in box_nms
URL: https://github.com/apache/incubator-mxnet/pull/14058#issuecomment-470430361
 
 
   @zhreshold My PR is complementary to improvements of @ptrendx on cuda 
kernels. After combining all three PRs, `box_nms` is close to the original 
`MultiBoxDetection` operator.


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[GitHub] [incubator-mxnet] arcadiaphy commented on issue #14058: add backgroud class in box_nms

2019-03-06 Thread GitBox 
arcadiaphy commented on issue #14058: add backgroud class in box_nms
URL: https://github.com/apache/incubator-mxnet/pull/14058#issuecomment-470061829
 
 
   @zhreshold Sometimes the validation time of SSD_512 on voc dataset is more 
than half an hour before, much longer than training time.
   
   I have written a simple script to show benchmark:
   ```
   import mxnet as mx
   import numpy as np
   import time
   
   np.random.seed(0)
   ctx = mx.gpu(0)
   
   def test_nms(background_ratio):
   batch_size = 32
   prior_number = 10  # SSD_512 has more than 100k prior boxes
   background_number = int(prior_number * background_ratio)
   data = np.zeros((batch_size, prior_number, 6))
   data[:, :, 0] = 0
   data[:, :background_number, 0] = -1
   data[:, :, 1] = np.random.random((batch_size, prior_number))
   
   xmin = np.random.random((batch_size, prior_number))
   ymin = np.random.random((batch_size, prior_number))
   width = np.random.random((batch_size, prior_number))
   height = np.random.random((batch_size, prior_number))
   data[:, :, 2] = xmin
   data[:, :, 3] = ymin
   data[:, :, 4] = xmin + width
   data[:, :, 5] = ymin + height
   
   mx_data = mx.nd.array(data, ctx=ctx)
   start = time.time()
   rv = mx.nd.contrib.box_nms(mx_data, overlap_thresh=0.5, valid_thresh=0, 
topk=400, id_index=0, score_index=1, coord_start=2, background_id=-1)
   mx.nd.waitall()
   print 'background ratio {} process time: {} s'.format(background_ratio, 
time.time() - start)
   
   
   test_nms(0)
   test_nms(0.5)
   test_nms(0.9)
   test_nms(0.99)
   ```
   
   The output:
   ```
   background ratio 0 process time: 22.8679060936 s
   background ratio 0.5 process time: 10.5633630753 s
   background ratio 0.9 process time: 1.97785997391 s
   background ratio 0.99 process time: 0.20063495636 s
   ```


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[GitHub] [incubator-mxnet] arcadiaphy commented on issue #14058: add backgroud class in box_nms

2019-03-06 Thread GitBox 
arcadiaphy commented on issue #14058: add backgroud class in box_nms
URL: https://github.com/apache/incubator-mxnet/pull/14058#issuecomment-470011391
 
 
   @zhreshold Currently there are no background_id logic in box_nms operator on 
prefiltering or nms step, so a lot of time is wasted in processing background 
boxes.
   
   To keep operator unchanged, background boxes need to be removed in NDArray 
input.


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[GitHub] [incubator-mxnet] arcadiaphy commented on issue #14058: add backgroud class in box_nms

2019-03-05 Thread GitBox 
arcadiaphy commented on issue #14058: add backgroud class in box_nms
URL: https://github.com/apache/incubator-mxnet/pull/14058#issuecomment-469982602
 
 
   @wkcn CI seems OK now, ready for merge.


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