[Enhance] Update numba version (#1043)

* update numba

* update

* fix numba version in doc

* fix typo

* fix_typos

docs/faq.md

@@ -19,12 +19,6 @@ We list some potential troubles encountered by users and developers, along with
 
   **NOTE**: We have migrated to use pycocotools in mmdet3d >= 0.13.0.
 
-- If you face the error shown below, and your environment contains numba == 0.48.0 with numpy >= 1.20.0:
-
-  ``TypeError: expected dtype object, got 'numpy.dtype[bool_]'``
-
-  please downgrade numpy to < 1.20.0 or install numba == 0.48 from source, because in numpy == 1.20.0, `np.dtype` produces subclass due to API change. Please refer to [here](https://github.com/numba/numba/issues/6041) for more details.
-
 - If you face the error shown below when importing pycocotools:
 
   ``ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject``
@@ -36,3 +30,4 @@ We list some potential troubles encountered by users and developers, along with
   or
 
   ``pip install -e "git+https://github.com/ppwwyyxx/cocoapi#egg=pycocotools&subdirectory=PythonAPI"``
+

docs_zh-CN/faq.md

@@ -19,12 +19,6 @@
 
   **注意**： 我们已经在 0.13.0 及之后的版本中全面支持 pycocotools。
 
-- 如果您遇到下面的问题，并且您的环境包含 numba == 0.48.0 和 numpy >= 1.20.0：
-
-  ``TypeError: expected dtype object, got 'numpy.dtype[bool_]'``
-
-    请将 numpy 的版本降级至 < 1.20.0，或者从源码安装 numba == 0.48，这是由于 numpy == 1.20.0 改变了 API，使得在调用 `np.dtype` 会产生子类。请参考 [这里](https://github.com/numba/numba/issues/6041) 获取更多细节。
-
 - 如果您在导入 pycocotools 相关包时遇到下面的问题：
 
   ``ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject``
@@ -36,3 +30,4 @@
   或者
 
   ``pip install -e "git+https://github.com/ppwwyyxx/cocoapi#egg=pycocotools&subdirectory=PythonAPI"``
+

mmdet3d/core/bbox/box_np_ops.py

@@ -705,7 +705,7 @@ def points_in_convex_polygon_3d_jit(points,
                                             normal_vec, d, num_surfaces)
 
 
-@numba.jit
+@numba.njit
 def points_in_convex_polygon_jit(points, polygon, clockwise=False):
     """Check points is in 2d convex polygons. True when point in polygon.
 
@@ -723,14 +723,16 @@ def points_in_convex_polygon_jit(points, polygon, clockwise=False):
     num_points_of_polygon = polygon.shape[1]
     num_points = points.shape[0]
     num_polygons = polygon.shape[0]
-    # if clockwise:
-    #     vec1 = polygon - polygon[:, [num_points_of_polygon - 1] +
-    #                              list(range(num_points_of_polygon - 1)), :]
-    # else:
-    #     vec1 = polygon[:, [num_points_of_polygon - 1] +
-    #                    list(range(num_points_of_polygon - 1)), :] - polygon
-    # vec1: [num_polygon, num_points_of_polygon, 2]
-    vec1 = np.zeros((2), dtype=polygon.dtype)
+    # vec for all the polygons
+    if clockwise:
+        vec1 = polygon - polygon[:,
+                                 np.array([num_points_of_polygon - 1] + list(
+                                     range(num_points_of_polygon - 1))), :]
+    else:
+        vec1 = polygon[:,
+                       np.array([num_points_of_polygon - 1] +
+                                list(range(num_points_of_polygon -
+                                           1))), :] - polygon
     ret = np.zeros((num_points, num_polygons), dtype=np.bool_)
     success = True
     cross = 0.0
@@ -738,12 +740,9 @@ def points_in_convex_polygon_jit(points, polygon, clockwise=False):
         for j in range(num_polygons):
             success = True
             for k in range(num_points_of_polygon):
-                if clockwise:
-                    vec1 = polygon[j, k] - polygon[j, k - 1]
-                else:
-                    vec1 = polygon[j, k - 1] - polygon[j, k]
-                cross = vec1[1] * (polygon[j, k, 0] - points[i, 0])
-                cross -= vec1[0] * (polygon[j, k, 1] - points[i, 1])
+                vec = vec1[j, k]
+                cross = vec[1] * (polygon[j, k, 0] - points[i, 0])
+                cross -= vec[0] * (polygon[j, k, 1] - points[i, 1])
                 if cross >= 0:
                     success = False
                     break

mmdet3d/core/evaluation/kitti_utils/rotate_iou.py

@@ -15,13 +15,13 @@ def div_up(m, n):
     return m // n + (m % n > 0)
 
 
-@cuda.jit('(float32[:], float32[:], float32[:])', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def trangle_area(a, b, c):
     return ((a[0] - c[0]) * (b[1] - c[1]) - (a[1] - c[1]) *
             (b[0] - c[0])) / 2.0
 
 
-@cuda.jit('(float32[:], int32)', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def area(int_pts, num_of_inter):
     area_val = 0.0
     for i in range(num_of_inter - 2):
@@ -31,7 +31,7 @@ def area(int_pts, num_of_inter):
     return area_val
 
 
-@cuda.jit('(float32[:], int32)', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def sort_vertex_in_convex_polygon(int_pts, num_of_inter):
     if num_of_inter > 0:
         center = cuda.local.array((2, ), dtype=numba.float32)
@@ -71,10 +71,7 @@ def sort_vertex_in_convex_polygon(int_pts, num_of_inter):
                 int_pts[j * 2 + 1] = ty
 
 
-@cuda.jit(
-    '(float32[:], float32[:], int32, int32, float32[:])',
-    device=True,
-    inline=True)
+@cuda.jit(device=True, inline=True)
 def line_segment_intersection(pts1, pts2, i, j, temp_pts):
     A = cuda.local.array((2, ), dtype=numba.float32)
     B = cuda.local.array((2, ), dtype=numba.float32)
@@ -117,10 +114,7 @@ def line_segment_intersection(pts1, pts2, i, j, temp_pts):
     return False
 
 
-@cuda.jit(
-    '(float32[:], float32[:], int32, int32, float32[:])',
-    device=True,
-    inline=True)
+@cuda.jit(device=True, inline=True)
 def line_segment_intersection_v1(pts1, pts2, i, j, temp_pts):
     a = cuda.local.array((2, ), dtype=numba.float32)
     b = cuda.local.array((2, ), dtype=numba.float32)
@@ -159,7 +153,7 @@ def line_segment_intersection_v1(pts1, pts2, i, j, temp_pts):
     return True
 
 
-@cuda.jit('(float32, float32, float32[:])', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def point_in_quadrilateral(pt_x, pt_y, corners):
     ab0 = corners[2] - corners[0]
     ab1 = corners[3] - corners[1]
@@ -178,7 +172,7 @@ def point_in_quadrilateral(pt_x, pt_y, corners):
     return abab >= abap and abap >= 0 and adad >= adap and adap >= 0
 
 
-@cuda.jit('(float32[:], float32[:], float32[:])', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def quadrilateral_intersection(pts1, pts2, int_pts):
     num_of_inter = 0
     for i in range(4):
@@ -202,7 +196,7 @@ def quadrilateral_intersection(pts1, pts2, int_pts):
     return num_of_inter
 
 
-@cuda.jit('(float32[:], float32[:])', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def rbbox_to_corners(corners, rbbox):
     # generate clockwise corners and rotate it clockwise
     angle = rbbox[4]
@@ -228,7 +222,7 @@ def rbbox_to_corners(corners, rbbox):
                 1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y
 
 
-@cuda.jit('(float32[:], float32[:])', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def inter(rbbox1, rbbox2):
     """Compute intersection of two rotated boxes.
 
@@ -254,7 +248,7 @@ def inter(rbbox1, rbbox2):
     return area(intersection_corners, num_intersection)
 
 
-@cuda.jit('(float32[:], float32[:], int32)', device=True, inline=True)
+@cuda.jit(device=True, inline=True)
 def devRotateIoUEval(rbox1, rbox2, criterion=-1):
     """Compute rotated iou on device.
 

mmdet3d/datasets/pipelines/data_augment_utils.py

@@ -2,7 +2,7 @@
 import numba
 import numpy as np
 import warnings
-from numba.errors import NumbaPerformanceWarning
+from numba.core.errors import NumbaPerformanceWarning
 
 from mmdet3d.core.bbox import box_np_ops
 

requirements/runtime.txt

 lyft_dataset_sdk
 networkx>=2.2,<2.3
-# we may unlock the version of numba in the future
-numba==0.48.0
-numpy<1.20.0
+numba>0.52.1
+numpy
 nuscenes-devkit
 plyfile
 scikit-image