icemelon9 commented on a change in pull request #4312: [TOPI][Relay][OP]
Dynamic NMS and strided_slice
URL: https://github.com/apache/incubator-tvm/pull/4312#discussion_r345405497
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File path: topi/python/topi/vision/nms.py
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@@ -289,8 +339,136 @@ def hybrid_nms(data, sorted_index, valid_count,
return output, box_indices
[email protected]
+def hybrid_dynamic_nms(data, sorted_index, max_output_size, score_threshold,
+ iou_threshold, score_index, zero, one):
+ """Hybrid routing for non-maximum suppression.
+
+ Parameters
+ ----------
+ data: tvm.Tensor or numpy NDArray
+ Bounding boxes with class and score. 3-D tensor with shape
+ [batch_size, num_anchors, 6] or [batch_size, num_anchors, 5].
+
+ sorted_index : tvm.Tensor or numpy NDArray
+ Bounding box indexes sorted by score, with shape
+ [batch_size, num_anchors].
+
+ max_output_size : tvm.const
+ Max number of output valid boxes for each instance.
+ By default all valid boxes are returned.
+
+ score_threshold : tvm.const
+ Lower limit of score for valid bounding boxes.
+
+ iou_threshold : tvm.const
+ Overlapping(IoU) threshold to suppress object with smaller score.
+
+ score_index: tvm.const
+ Index of the scores/confidence of boxes.
+
+ zero: tvm.const
+ Constant zero with the same dtype as data.
+
+ one: tvm.const
+ Constant one with the same dtype as data.
+
+ Returns
+ -------
+ box_indices: tvm.Tensor
+ 2-D tensor with shape [batch_size, num_anchors].
+ """
+
+
+ batch_size = data.shape[0]
+ num_anchors = data.shape[1]
+ box_data_length = data.shape[2]
+
+ # box_indices is the expected value, similar to TF & ONNX
+ box_indices = output_tensor((batch_size, num_anchors), sorted_index.dtype)
+ output = output_tensor((batch_size,
+ num_anchors,
+ box_data_length,), data.dtype)
+
+ for i in range(batch_size):
+ if iou_threshold > 0:
+ # Reorder output
+ for j in parallel(num_anchors):
+ for k in range(box_data_length):
+ output[i, j, k] = data[i, sorted_index[i, j], k]
+ if output[i, j, score_index] > score_threshold:
+ box_indices[i, j] = sorted_index[i, j]
+ else:
+ box_indices[i, j] = -1
+
+ # Apply nms
+ box_start_idx = 1
+ batch_idx = i
+
+ for j in range(num_anchors):
+ # index sorted
+ j_sorted = sorted_index[i, j]
+
+ box_a_idx = j
+ # l: left, t: top, r: right, b: bottom
+ a_l = min(output[batch_idx, box_a_idx, box_start_idx],
+ output[batch_idx, box_a_idx, box_start_idx + 2])
+ a_t = min(output[batch_idx, box_a_idx, box_start_idx + 1],
+ output[batch_idx, box_a_idx, box_start_idx + 3])
+ a_r = max(output[batch_idx, box_a_idx, box_start_idx],
+ output[batch_idx, box_a_idx, box_start_idx + 2])
+ a_b = max(output[batch_idx, box_a_idx, box_start_idx + 1],
+ output[batch_idx, box_a_idx, box_start_idx + 3])
+
+ for k in parallel(j + 1, num_anchors):
+ k_sorted = sorted_index[i, k]
+ box_b_idx = k
+ # l: left, t: top, r: right, b: bottom
+ b_l = min(output[batch_idx, box_b_idx, box_start_idx],
+ output[batch_idx, box_b_idx, box_start_idx + 2])
+ b_t = min(output[batch_idx, box_b_idx, box_start_idx + 1],
+ output[batch_idx, box_b_idx, box_start_idx + 3])
+ b_r = max(output[batch_idx, box_b_idx, box_start_idx],
+ output[batch_idx, box_b_idx, box_start_idx + 2])
+ b_b = max(output[batch_idx, box_b_idx, box_start_idx + 1],
+ output[batch_idx, box_b_idx, box_start_idx + 3])
+
+ # Overlapping width and height
+ w = max(zero, min(a_r, b_r) - max(a_l, b_l))
+ h = max(zero, min(a_b, b_b) - max(a_t, b_t))
+
+ # Overlapping area
+ area = h * w
+
+ # total area of the figure formed by box a and box b
except for overlapping area
+ u = (a_r - a_l) * (a_b - a_t) + (b_r - b_l) * (b_b - b_t)
- area
+
+ # get the iou
+ iou = area / u
+
+ # output[i, k, sorted_index] = iou
+
+ if iou >= score_threshold:
+ box_indices[i, k] = -1
+
+ else:
+ for j in parallel(num_anchors):
+ box_indices[i, j] = sorted_index[i, j]
+
+ # Only return max_output_size valid boxes
+ num_valid_boxes = 0
+ if max_output_size > 0:
+ for j in parallel(num_anchors):
+ if num_valid_boxes == max_output_size:
+ box_indices[i, j] = -1
+ else:
+ num_valid_boxes += 1
+
+ return output, box_indices
+
+
@tvm.target.generic_func
-def non_max_suppression(data, valid_count, max_output_size=-1,
+def non_max_suppression(data, valid_count, max_output_size=-1,
score_threshold=0.0,
iou_threshold=0.5, force_suppress=False, top_k=-1,
coord_start=2, score_index=1, id_index=0,
return_indices=True, invalid_to_bottom=False):
Review comment:
Change the arg name `return_indeces`
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