@@ -43,7 +43,6 @@ __all__ = [ | |||||
"logsoftmax", | "logsoftmax", | ||||
"matmul", | "matmul", | ||||
"max_pool2d", | "max_pool2d", | ||||
"nms", | |||||
"one_hot", | "one_hot", | ||||
"prelu", | "prelu", | ||||
"roi_align", | "roi_align", | ||||
@@ -1482,7 +1481,7 @@ def nms(boxes: Tensor, scores: Tensor, iou_thresh: float) -> Tensor: | |||||
x[:,2:] = np.random.rand(100,2)*20 + 100 | x[:,2:] = np.random.rand(100,2)*20 + 100 | ||||
scores = tensor(np.random.rand(100)) | scores = tensor(np.random.rand(100)) | ||||
inp = tensor(x) | inp = tensor(x) | ||||
result = F.nms(inp, scores, iou_thresh=0.7) | |||||
result = F.nn.nms(inp, scores, iou_thresh=0.7) | |||||
print(result.numpy()) | print(result.numpy()) | ||||
Outputs: | Outputs: | ||||
@@ -357,7 +357,7 @@ def test_nms(): | |||||
) | ) | ||||
inp = tensor(x) | inp = tensor(x) | ||||
scores = tensor([0.5, 0.8, 0.9, 0.6], dtype=np.float32) | scores = tensor([0.5, 0.8, 0.9, 0.6], dtype=np.float32) | ||||
result = F.nms(inp, scores=scores, iou_thresh=0.5) | |||||
result = F.nn.nms(inp, scores=scores, iou_thresh=0.5) | |||||
np.testing.assert_equal(result.numpy(), np.array([2, 1, 3], dtype=np.int32)) | np.testing.assert_equal(result.numpy(), np.array([2, 1, 3], dtype=np.int32)) | ||||