From af349d6102bd17be3881ae9ccdee057bf4ab546b Mon Sep 17 00:00:00 2001
From: Megvii Engine Team <megengine@megvii.com>
Date: Tue, 29 Sep 2020 14:48:57 +0800
Subject: [PATCH] fix(mge/functional): fix op mismatch when tracing NMSKeep

GitOrigin-RevId: e8f2cbb7557b7482df936faca80f4fcc15eef22b
---
 imperative/python/megengine/functional/nn.py | 18 +++++++++++++++---
 imperative/python/megengine/jit/tracing.py   |  7 +++++++
 imperative/python/test/unit/test_tracing.py  | 22 ++++++++++++++++++++++
 3 files changed, 44 insertions(+), 3 deletions(-)

diff --git a/imperative/python/megengine/functional/nn.py b/imperative/python/megengine/functional/nn.py
index 6ba8ee0e..7d120b8e 100644
--- a/imperative/python/megengine/functional/nn.py
+++ b/imperative/python/megengine/functional/nn.py
@@ -17,6 +17,7 @@ from ..core.tensor import megbrain_graph, utils
 from ..core.tensor.core import TensorBase, TensorWrapperBase, apply
 from ..core.tensor.utils import astensor1d
 from ..distributed import WORLD, is_distributed
+from ..jit.tracing import is_tracing
 from ..random import uniform
 from ..tensor import Tensor
 from .debug_param import get_conv_execution_strategy
@@ -1470,13 +1471,17 @@ def indexing_one_hot(
     return result
 
 
-def nms(boxes: Tensor, scores: Tensor, iou_thresh: float) -> Tensor:
+def nms(
+    boxes: Tensor, scores: Tensor, iou_thresh: float, max_output: Optional[int] = None
+) -> Tensor:
     r"""
     Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union(IoU).
 
     :param boxes: tensor of shape `(N, 4)`; the boxes to perform nms on; each box is expected to be in `(x1, y1, x2, y2)` format.
     :param iou_thresh: IoU threshold for overlapping.
     :param scores: tensor of shape `(N,)`, the score of boxes.
+    :param max_output: the maximum number of boxes to keep; it is optional if this operator is not traced 
+        otherwise it required to be specified; if it is not specified, all boxes are kept.
     :return: indices of the elements that have been kept by NMS.
 
     Examples:
@@ -1515,12 +1520,19 @@ def nms(boxes: Tensor, scores: Tensor, iou_thresh: float) -> Tensor:
     scores = scores.detach()
     sorted_idx = argsort(scores, descending=True)
     boxes = boxes[sorted_idx]
-    max_output = boxes.shape[0]
+
+    if is_tracing():
+        assert (
+            max_output is not None and max_output > 0
+        ), "max_output should be specified under tracing"
+
+    if max_output is None:
+        max_output = boxes.shape[0]
 
     op = builtin.NMSKeep(iou_thresh, max_output)
     inp = utils.convert_inputs(boxes.reshape(1, -1, 4))
     indices, count = apply(op, *inp)
-    indices = indices[0][: count.item()]
+    indices = indices[0][: count[0]]
     keep_inds = sorted_idx[indices]
     return keep_inds
 
diff --git a/imperative/python/megengine/jit/tracing.py b/imperative/python/megengine/jit/tracing.py
index 1fe93c71..6fe59b46 100644
--- a/imperative/python/megengine/jit/tracing.py
+++ b/imperative/python/megengine/jit/tracing.py
@@ -36,6 +36,13 @@ active_trace = None
 skip_tracing = False
 
 
+def is_tracing():
+    if active_trace is None:
+        return False
+    else:
+        return not skip_tracing
+
+
 @contextlib.contextmanager
 def exclude_from_trace():
     global skip_tracing
diff --git a/imperative/python/test/unit/test_tracing.py b/imperative/python/test/unit/test_tracing.py
index d54206d9..805d0121 100644
--- a/imperative/python/test/unit/test_tracing.py
+++ b/imperative/python/test/unit/test_tracing.py
@@ -357,3 +357,25 @@ def test_trace_broadcast():
         f(x1)
         f(x2)
         f(x3)
+
+
+def test_trace_nms():
+    def make_inputs(n):
+        boxes = np.zeros((n, 4))
+        boxes[:, :2] = np.random.rand(n, 2) * 100
+        boxes[:, 2:] = np.random.rand(n, 2) * 100 + 100
+
+        scores = np.random.rand(n)
+
+        return tensor(boxes), tensor(scores)
+
+    @trace(symbolic=False)
+    def f(boxes, scores):
+        results = F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
+        with exclude_from_trace():
+            _ = F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
+        return results
+
+    f(*make_inputs(10))
+    f(*make_inputs(20))
+    f(*make_inputs(30))