diff --git a/lib/rpn/proposal_layer.py b/lib/rpn/proposal_layer.py
index 7bc4ace..97854d3 100644
--- a/lib/rpn/proposal_layer.py
+++ b/lib/rpn/proposal_layer.py
@@ -221,134 +221,6 @@ def forward(self, bottom, top):
         top[0].reshape(*(blob.shape))
         top[0].data[...] = blob
             
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-    #     p2_shape =  bottom[3].data.shape
-    #     p3_shape =  bottom[4].data.shape
-    #     p4_shape =  bottom[5].data.shape
-    #     p5_shape =  bottom[6].data.shape
-    #     p6_shape =  bottom[7].data.shape
-    #     feat_shape = []
-    #     feat_shape.append(p2_shape)
-    #     feat_shape.append(p3_shape)
-    #     feat_shape.append(p4_shape)
-    #     feat_shape.append(p5_shape)
-    #     feat_shape.append(p6_shape)  
-        
-    #     num_feat = len(feat_shape)#[1,5,4]
-    #     score_index_start=0
-    #     bbox_index_start=0
-    #     keep_proposal = []
-    #     keep_scores = []
-    # #########################
-
-    #     for i in range(num_feat):
-    #         feat_stride = int(self._feat_stride[i])#4,8,16,32,64
-    #         #print 'feat_stride:', feat_stride
-    #         anchor = generate_anchors(base_size=feat_stride, ratios=self._ratios, scales=self._scales)
-    #         num_anchors = anchor.shape[0]#3
-    #         height = feat_shape[i][2]
-    #         width = feat_shape[i][3]
-    #         shift_x = np.arange(0, width) * feat_stride
-    #         shift_y = np.arange(0, height) * feat_stride
-    #         shift_x, shift_y = np.meshgrid(shift_x, shift_y)
-    #         shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
-    #         A = num_anchors#3
-    #         K = shifts.shape[0]#height*width
-    #         anchors = anchor.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
-    #         anchors = anchors.reshape((K * A, 4))#3*height*widht,4
-    #         print scores_list.shape
-           
-    #         scores = scores_list[:,:,int(score_index_start):int(score_index_start+K*A)]
-           
-    #         bbox_deltas = bbox_deltas_list[int(bbox_index_start):int(bbox_index_start+K*A),:]
-    #         score_index_start += K*A
-    #         bbox_index_start += K*A
-
-    #         bbox_deltas = clip_pad(bbox_deltas, (height, width))
-    #         bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))#[1,h,w,12]--->[1*h*w*3,4]
-    #         scores = clip_pad(scores, (height, width))
-    #         scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))#[1,h,w,3]--->[1*h*w*3,1]
-    #         proposals =  bbox_transform_inv(anchors, bbox_deltas)#debug here, corresponding?
-    #         proposals = clip_boxes(proposals, im_info[:2])
-     
-    #         keep = _filter_boxes(proposals, self._min_sizes[i] * im_info[2])
-        
-    #         keep_proposal.append(proposals[keep, :])
-    #         keep_scores.append(scores[keep])
-    #     proposals = keep_proposal[0]
-    #     scores = keep_scores[0]
-    #     for i in range(1,num_feat):
-    #         proposals=np.vstack((proposals, keep_proposal[i]))
-    #         scores=np.vstack((scores, keep_scores[i]))
-    #     #print 'roi concate t_1 spends :{:.4f}s'.format(time.time()-t_1)
-    #     # 4. sort all (proposal, score) pairs by score from highest to lowest
-    #     # 5. take top pre_nms_topN (e.g. 6000)
-    #     #t_2 = time.time()
-    #     order = scores.ravel().argsort()[::-1]
-    #     if pre_nms_topN > 0:
-    #         order = order[:pre_nms_topN]
-    #     proposals = proposals[order, :]
-    #     scores = scores[order]
-    #     #print 'roi concate t_2_1_1 spends :{:.4f}s'.format(time.time()-t_2)
-    #     # 6. apply nms (e.g. threshold = 0.7)
-    #     # 7. take after_nms_topN (e.g. 300)
-    #     # 8. return the top proposals (-> RoIs top)
-    #     #t_nms = time.time()
-    #     det = np.hstack((proposals, scores)).astype(np.float32)
-    #     keep = nms(det,nms_thresh)
-    #     #print 'roi concate nms spends :{:.4f}s'.format(time.time()-t_nms)
-    #     print len(keep)
-
-    #     if post_nms_topN > 0:
-    #         keep = keep[:post_nms_topN]
-     
-    #     # pad to ensure output size remains unchanged
-    #     # if len(keep) < post_nms_topN:
-    #     #     try:
-    #     #         pad = npr.choice(keep, size=post_nms_topN - len(keep))
-    #     #     except:
-    #     #         proposals = np.zeros((post_nms_topN, 4), dtype=np.float32)
-    #     #         proposals[:,2] = 16
-    #     #         proposals[:,3] = 16
-    #     #         batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
-    #     #         blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
-    #     #         top[0].reshape(*(blob.shape))
-    #     #         top[0].data[...] = blob
-    #     #         return
-    #     #     keep = np.hstack((keep, pad))
-
-    #     proposals = proposals[keep, :]
-    #     scores = scores[keep]
-    #     #print 'roi concate t_2 spends :{:.4f}s'.format(time.time()-t_2)
-    #     # Output rois array
-    #     # Our RPN implementation only supports a single input image, so all
-    #     # batch inds are 0
-    #     batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
-    #     blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
-    
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-    #     top[0].reshape(*(blob.shape))
-    #     top[0].data[...] = blob
 
     def backward(self, top, propagate_down, bottom):
         """This layer does not propagate gradients."""