(self, is_train, req, in_data, out_data, aux)
| 44 | print self._anchors |
| 45 | |
| 46 | def forward(self, is_train, req, in_data, out_data, aux): |
| 47 | nms = gpu_nms_wrapper(self._threshold, in_data[0].context.device_id) |
| 48 | |
| 49 | batch_size = in_data[0].shape[0] |
| 50 | if batch_size > 1: |
| 51 | raise ValueError("Sorry, multiple images each device is not implemented") |
| 52 | |
| 53 | # for each (H, W) location i |
| 54 | # generate A anchor boxes centered on cell i |
| 55 | # apply predicted bbox deltas at cell i to each of the A anchors |
| 56 | # clip predicted boxes to image |
| 57 | # remove predicted boxes with either height or width < threshold |
| 58 | # sort all (proposal, score) pairs by score from highest to lowest |
| 59 | # take top pre_nms_topN proposals before NMS |
| 60 | # apply NMS with threshold 0.7 to remaining proposals |
| 61 | # take after_nms_topN proposals after NMS |
| 62 | # return the top proposals (-> RoIs top, scores top) |
| 63 | |
| 64 | pre_nms_topN = self._rpn_pre_nms_top_n |
| 65 | post_nms_topN = self._rpn_post_nms_top_n |
| 66 | min_size = self._rpn_min_size |
| 67 | |
| 68 | # the first set of anchors are background probabilities |
| 69 | # keep the second part |
| 70 | scores = in_data[0].asnumpy()[:, self._num_anchors:, :, :] |
| 71 | bbox_deltas = in_data[1].asnumpy() |
| 72 | im_info = in_data[2].asnumpy()[0, :] |
| 73 | |
| 74 | if DEBUG: |
| 75 | print 'im_size: ({}, {})'.format(im_info[0], im_info[1]) |
| 76 | print 'scale: {}'.format(im_info[2]) |
| 77 | |
| 78 | # 1. Generate proposals from bbox_deltas and shifted anchors |
| 79 | # use real image size instead of padded feature map sizes |
| 80 | height, width = int(im_info[0] / self._feat_stride), int(im_info[1] / self._feat_stride) |
| 81 | |
| 82 | if DEBUG: |
| 83 | print 'score map size: {}'.format(scores.shape) |
| 84 | print "resudial: {}".format((scores.shape[2] - height, scores.shape[3] - width)) |
| 85 | |
| 86 | # Enumerate all shifts |
| 87 | shift_x = np.arange(0, width) * self._feat_stride |
| 88 | shift_y = np.arange(0, height) * self._feat_stride |
| 89 | shift_x, shift_y = np.meshgrid(shift_x, shift_y) |
| 90 | shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() |
| 91 | |
| 92 | # Enumerate all shifted anchors: |
| 93 | # |
| 94 | # add A anchors (1, A, 4) to |
| 95 | # cell K shifts (K, 1, 4) to get |
| 96 | # shift anchors (K, A, 4) |
| 97 | # reshape to (K*A, 4) shifted anchors |
| 98 | A = self._num_anchors |
| 99 | K = shifts.shape[0] |
| 100 | anchors = self._anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)) |
| 101 | anchors = anchors.reshape((K * A, 4)) |
| 102 | |
| 103 | # Transpose and reshape predicted bbox transformations to get them |
nothing calls this directly
no test coverage detected