[GitHub] [tvm] yongwww commented on a change in pull request #7137: [Torch] Fix PyTorch NMS conversion for negative scores

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

yongwww commented on a change in pull request #7137:
URL: https://github.com/apache/tvm/pull/7137#discussion_r548404381



##########
File path: tests/python/frontend/pytorch/test_object_detection.py
##########
@@ -70,7 +71,7 @@ def generate_jit_model(index):
     ]
 
     model_func = model_funcs[index]
-    model = TraceWrapper(model_func(pretrained=True))
+    model = TraceWrapper(model_func(pretrained=True, rpn_pre_nms_top_n_test=200))

Review comment:
       Glad to see `rpn_pre_nms_top_n_test ` is able to limit the proposals before nms. I am not sure if the parameter is specified for real use cases, seems using default of the parameter to do benchamarking makes more sense to me

##########
File path: python/tvm/relay/frontend/pytorch.py
##########
@@ -1857,16 +1857,18 @@ def nms(self, inputs, input_types):
         scores = inputs[1]
         iou_threshold = inputs[2]
 
+        num_boxes = _op.shape_of(scores)
+
+        # TVM NMS assumes score > 0
+        scores = scores - _op.min(scores) + _op.const(1.0)
         # Generate data with shape (1, num_anchors, 5)
         scores = AttrCvt(op_name="expand_dims", extras={"axis": -1, "num_newaxis": 1})([scores], {})
-
-        # Prepare input data for get_valid_counts
         data = _op.concatenate([scores, boxes], -1)
         data = _op.expand_dims(data, 0, 1)
-        # Leverage get_valid_counts to sort the data and clear invalid boxes
-        ct, data, indices = get_relay_op("get_valid_counts")(
-            data, score_threshold=-1.0, id_index=-1, score_index=0
-        )
+        # PyTorch NMS doesn't have score_threshold, so no need to run get_valid_count

Review comment:
       then in this case, the nms will compute for all boxes, the perf should be bad. Not sure how PyTorch speeds up the computation for boxes with negative score.




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