[GitHub] wkcn opened a new pull request #9939: add multi proposal operator (cpu version)

[GitBox <gi...@apache.org>]

wkcn opened a new pull request #9939: add multi proposal operator (cpu version)
URL: https://github.com/apache/incubator-mxnet/pull/9939
 
 
   ## Description ##
   The multi_proposal operator (`mxnet.sym.contrib.MultiProposal`, CPU version) is not implemented before.
   I wrote the code about it.
   I didn't change the author list. Could I add my name on it?
   
   ## Checklist ##
   ### Essentials ###
   - [x] Passed code style checking (`make lint`)
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments are documented. 
   - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
   - [x] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [x] Multi Proposal Operator is supported on the CPU context.
   
   ## Comments ##
   Here is the testing code.
   ```python
   import mxnet as mx
   from mxnet import nd
   import numpy as np
   
   feature_stride = 16 
   scales = (8, 16, 32)
   ratios = (0.5, 1, 2)
   rpn_pre_nms_top_n = 12000 
   rpn_post_nms_top_n = 2000
   threshold = 0.7
   rpn_min_size = 16
   
   batch_size = 20
   feat_len = 14
   H, W = feat_len, feat_len
   num_anchors = len(scales) * len(ratios)
   count_anchors = feat_len * feat_len * num_anchors
   
   '''
       cls_prob: (batch_size, 2 * num_anchors, H, W)
       bbox_pred: (batch_size, 4 * num_anchors, H, W) 
       im_info: (batch_size, 3)
   '''
   
   cls_prob = nd.empty((batch_size, 2 * num_anchors, H, W), dtype = np.float32) 
   bbox_pred = nd.empty((batch_size, 4 * num_anchors, H, W), dtype = np.float32) 
   im_info = nd.empty((batch_size, 3), dtype = np.float32) 
   
   cls_prob = nd.array(np.random.random(cls_prob.shape))
   bbox_pred = nd.array(np.random.random(bbox_pred.shape))
   
   for i in range(batch_size):
       im_size = np.random.randint(100, feat_len * feature_stride, size = (2,))
       im_scale = np.random.randint(70, 100) / 100.0
       im_info[i, :] = [im_size[0], im_size[1], im_scale]
   
   def get_sub(arr, i):
       new_shape = list(arr.shape)
       new_shape[0] = 1
       res = arr[i].reshape(new_shape)
       return res
   
   def test(rpn_pre_nms_top_n, rpn_post_nms_top_n):
   
       single_proposal = []
       single_score = []
       for i in range(batch_size):
           rois, score = mx.nd.contrib.Proposal(
                   cls_score = get_sub(cls_prob, i),
                   bbox_pred = get_sub(bbox_pred, i),
                   im_info = get_sub(im_info, i),
                   feature_stride = feature_stride,
                   scales = scales,
                   ratios = ratios,
                   rpn_pre_nms_top_n = rpn_pre_nms_top_n,
                   rpn_post_nms_top_n = rpn_post_nms_top_n,
                   threshold = threshold,
                   rpn_min_size = rpn_min_size, output_score = True)
           single_proposal.append(rois)
           single_score.append(score)
   
       multi_proposal, multi_score = mx.nd.contrib.MultiProposal(
               cls_score = cls_prob,
               bbox_pred = bbox_pred,
               im_info = im_info,
               feature_stride = feature_stride,
               scales = scales,
               ratios = ratios,
               rpn_pre_nms_top_n = rpn_pre_nms_top_n,
               rpn_post_nms_top_n = rpn_post_nms_top_n,
               threshold = threshold,
               rpn_min_size = rpn_min_size, output_score = True)
   
       single_proposal = nd.stack(*single_proposal).reshape(multi_proposal.shape)
       single_score = nd.stack(*single_score).reshape(multi_score.shape)
   
       single_proposal_np = single_proposal.asnumpy()
       multi_proposal_np = multi_proposal.asnumpy()
   
       single_score_np = single_score.asnumpy()
       multi_score_np = multi_score.asnumpy()
       print (multi_proposal_np.shape)
   
       # test rois x1,y1,x2,y2
       assert np.allclose(single_proposal_np[:, 1:], multi_proposal_np[:, 1:])
       # test rois batch_idx 
       for i in range(batch_size):
           start = i * rpn_post_nms_top_n
           end = start + rpn_post_nms_top_n
           assert (multi_proposal_np[start:end, 0] == i).all()
   
       # test score
       assert np.allclose(single_score_np, multi_score_np)
   
   test(rpn_pre_nms_top_n, rpn_post_nms_top_n)
   test(rpn_pre_nms_top_n, 1500)
   test(1000, 500)
   print ("test ok")
   
   ```

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