wkcn commented on issue #9939: add multi proposal operator (cpu version) and fix the bug in proposal op (gpu version) URL: https://github.com/apache/incubator-mxnet/pull/9939#issuecomment-369797499 I wrote a cpu/gpu consistency test for Proposal and MultiProposal. I found there is difference between the CPU output and the GPU output for mx.nd.contrib.Proposal. It seems that the index order of the Non-Maximum-Suppression result may be different between the CPU implementation and the GPU implementation. And another problem is that it may need to add the condition `num_to_keep < rpn_post_nms_top_n` https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/proposal.cu#L341 reference: https://github.com/apache/incubator-mxnet/blob/master/src/operator/contrib/proposal.cc#L235 Here is the cpu/gpu consistency test. ```python import mxnet as mx import numpy as np # @with_seed() def test_multi_proposal_op(): # paramters 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 feat_len = 14 H, W = feat_len, feat_len num_anchors = len(scales) * len(ratios) count_anchors = H * W * num_anchors def get_new_data(batch_size, ctx): ''' 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 = mx.nd.empty((batch_size, 2 * num_anchors, H, W), dtype = np.float32, ctx = ctx) bbox_pred = mx.nd.empty((batch_size, 4 * num_anchors, H, W), dtype = np.float32, ctx = ctx) im_info = mx.nd.empty((batch_size, 3), dtype = np.float32, ctx = ctx) cls_prob = mx.nd.array(np.random.random(cls_prob.shape), ctx = ctx) bbox_pred = mx.nd.array(np.random.random(bbox_pred.shape), ctx = ctx) 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] return cls_prob, bbox_pred, im_info def check_proposal_consistency(op, batch_size): ''' op is mx.nd.contrib.Proposal or mx.nd.contrib.MultiProposal ''' cls_prob, bbox_pred, im_info = get_new_data(batch_size, mx.cpu(0)) rois_cpu, score_cpu = op( 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) gpu_ctx = mx.gpu(0) # copy data to gpu from cpu cls_prob_gpu = cls_prob.as_in_context(gpu_ctx) bbox_pred_gpu = bbox_pred.as_in_context(gpu_ctx) im_info_gpu = im_info.as_in_context(gpu_ctx) rois_gpu, score_gpu = op( cls_score = cls_prob_gpu, bbox_pred = bbox_pred_gpu, im_info = im_info_gpu, 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) print (rois_cpu.asnumpy(), rois_gpu.asnumpy()) assert np.allclose(rois_cpu.asnumpy(), rois_gpu.asnumpy()) assert np.allclose(score_cpu.asnumpy(), score_gpu.asnumpy()) check_proposal_consistency(mx.nd.contrib.Proposal, 1) check_proposal_consistency(mx.nd.contrib.MultiProposal, 20) test_multi_proposal_op() print ("test ok") ```
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