TypeError: list indices must be integers or slices, not cupy.core.core.ndarray
TypeError: list indices must be integers or slices, not cupy.core.core.ndarray
在物体检测算法中，非最大抑制（NMS）用于丢弃物体的额外检测结果，例如一辆车。
通常情况下，水平边界框用于对象检测算法，水平 NMS 的 GPU 实现已经存在，但我想要旋转边界框的 GPU 实现。
CPU 实现已经完成，但我正在努力使用 CuPy 包将 CPU 版本转换为 GPU 版本。这是我写的代码。代码段后可以看到报错。
我的问题是 TypeError: list indices must be integers or slice, not cupy.core.core.ndarray 的原因是什么？
from shapely.geometry import Polygon as shpoly
import time
#### CPU implementation
import numpy as np
def polygon_iou(poly1, poly2):
"""
Intersection over union between two shapely polygons.
"""
if not poly1.intersects(poly2): # this test is fast and can accelerate calculation
iou = 0
else:
try:
inter_area = poly1.intersection(poly2).area
union_area = poly1.area + poly2.area - inter_area
iou = float(inter_area) / float(union_area)
except shapely.geos.TopologicalError:
warnings.warn("'shapely.geos.TopologicalError occured, iou set to 0'", UserWarning)
iou = 0
except ZeroDivisionError:
iou = 0
return iou
def polygon_from_array(poly_):
"""
Create a shapely polygon object from gt or dt line.
"""
polygon_points = np.array(poly_).reshape(4, 2)
polygon = shpoly(polygon_points).convex_hull
return polygon
def nms(dets, thresh):
scores = dets[:, 8]
order = scores.argsort()[::-1]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polygon_from_array(dets[i,:8])
polys.append(tm_polygon)
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polygon_iou(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
#### GPU implementation
import cupy as cp
def polygon_iou_gpu(poly1, poly2):
"""
Intersection over union between two shapely polygons.
"""
if not poly1.intersects(poly2): # this test is fast and can accelerate calculation
iou = 0
else:
try:
inter_area = poly1.intersection(poly2).area
union_area = poly1.area + poly2.area - inter_area
iou = float(inter_area) / float(union_area)
except shapely.geos.TopologicalError:
warnings.warn("'shapely.geos.TopologicalError occured, iou set to 0'", UserWarning)
iou = 0
except ZeroDivisionError:
iou = 0
return iou
def polygon_from_array_gpu(poly_):
"""
Create a shapely polygon object from gt or dt line.
"""
polygon_points = cp.array(poly_).reshape(4, 2)
polygon = shpoly(polygon_points).convex_hull
return polygon
def nms_gpu(dets, thresh):
scores = dets[:, 8]
order = scores.argsort()[::-1]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polygon_from_array_gpu(dets[i,:8])
polys.append(tm_polygon)
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polygon_iou_gpu(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
if __name__ == '__main__':
import random
boxes = np.random.randint(0,100,(1000,8))
scores = np.random.rand(1000, 1)
dets = np.hstack((boxes, scores[:])).astype(np.float32)
thresh = 0.1
start = time.time()
keep = nms(dets, thresh)
print("CPU implementation took: {}".format(time.time() - start))
cp.cuda.Device(1)
dets_gpu = cp.array(dets)
start = time.time()
keep = nms_gpu(dets_gpu, thresh)
print("GPU implementation took: {}".format(time.time() - start))
错误是
CPU implementation took: 0.3672311305999756
Traceback (most recent call last):
File "nms_rotated.py", line 117, in
keep = nms_gpu(dets_gpu, thresh)
File "nms_rotated.py", line 97, in nms_gpu
iou = polygon_iou_gpu(polys[i], polys[order[j + 1]])
TypeError: list indices must be integers or slices, not cupy.core.core.ndarray
更新：2019 年 2 月 13 日
我尝试了@Yuki Hashimoto 的回答
将 iou = polygon_iou_gpu(polys[i], polys[order[j + 1]]) 替换为 iou = polygon_iou_gpu(polys[i.get()], polys[order[j + 1].get()])。它不会抛出任何错误，但 GPU 版本比 CPU 版本慢数倍。
通过使用 100000 次随机检测：
CPU implementation took: 47.125494956970215
GPU implementation took: 142.08464860916138
[更新 2019/2/13]
请参考@yuki-hashimoto 的回答，比较合适
如错误消息中所写
TypeError: list indices must be integers or slices, not cupy.core.core.ndarray
我猜 order 是 cupy 数组？
在那种情况下，polys[order[j + 1]] 正在使用索引 order[j+1] 作为 cupy 数组，这可能会导致问题。
如何尝试通过 cuda.to_cpu(array) 方法将它们转换为 numpy 数组？
from chainer import cuda
iou = polygon_iou_gpu(polys[i], polys[cuda.to_cpu(order[j + 1])])
简而言之：使用PFN的官方non-maximum suppression。
详情：
使用 cp.where，其中 returns 一个符合某些条件的 list 对象。
不推荐corochann的答案，因为polys是一个列表，list也不应该被np.ndarray分割。 （并且不推荐注入另一个依赖...）
>>> polys[order.get()] # get method returns np.ndarray
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: only integer scalar arrays can be converted to a scalar index
>>> polys[order[j + 1].get()]
### some result in some case, but this may fails depending on your env.###
在物体检测算法中，非最大抑制（NMS）用于丢弃物体的额外检测结果，例如一辆车。
通常情况下，水平边界框用于对象检测算法，水平 NMS 的 GPU 实现已经存在，但我想要旋转边界框的 GPU 实现。
CPU 实现已经完成，但我正在努力使用 CuPy 包将 CPU 版本转换为 GPU 版本。这是我写的代码。代码段后可以看到报错。
我的问题是 TypeError: list indices must be integers or slice, not cupy.core.core.ndarray 的原因是什么？
from shapely.geometry import Polygon as shpoly
import time
#### CPU implementation
import numpy as np
def polygon_iou(poly1, poly2):
"""
Intersection over union between two shapely polygons.
"""
if not poly1.intersects(poly2): # this test is fast and can accelerate calculation
iou = 0
else:
try:
inter_area = poly1.intersection(poly2).area
union_area = poly1.area + poly2.area - inter_area
iou = float(inter_area) / float(union_area)
except shapely.geos.TopologicalError:
warnings.warn("'shapely.geos.TopologicalError occured, iou set to 0'", UserWarning)
iou = 0
except ZeroDivisionError:
iou = 0
return iou
def polygon_from_array(poly_):
"""
Create a shapely polygon object from gt or dt line.
"""
polygon_points = np.array(poly_).reshape(4, 2)
polygon = shpoly(polygon_points).convex_hull
return polygon
def nms(dets, thresh):
scores = dets[:, 8]
order = scores.argsort()[::-1]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polygon_from_array(dets[i,:8])
polys.append(tm_polygon)
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polygon_iou(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
#### GPU implementation
import cupy as cp
def polygon_iou_gpu(poly1, poly2):
"""
Intersection over union between two shapely polygons.
"""
if not poly1.intersects(poly2): # this test is fast and can accelerate calculation
iou = 0
else:
try:
inter_area = poly1.intersection(poly2).area
union_area = poly1.area + poly2.area - inter_area
iou = float(inter_area) / float(union_area)
except shapely.geos.TopologicalError:
warnings.warn("'shapely.geos.TopologicalError occured, iou set to 0'", UserWarning)
iou = 0
except ZeroDivisionError:
iou = 0
return iou
def polygon_from_array_gpu(poly_):
"""
Create a shapely polygon object from gt or dt line.
"""
polygon_points = cp.array(poly_).reshape(4, 2)
polygon = shpoly(polygon_points).convex_hull
return polygon
def nms_gpu(dets, thresh):
scores = dets[:, 8]
order = scores.argsort()[::-1]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polygon_from_array_gpu(dets[i,:8])
polys.append(tm_polygon)
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polygon_iou_gpu(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
if __name__ == '__main__':
import random
boxes = np.random.randint(0,100,(1000,8))
scores = np.random.rand(1000, 1)
dets = np.hstack((boxes, scores[:])).astype(np.float32)
thresh = 0.1
start = time.time()
keep = nms(dets, thresh)
print("CPU implementation took: {}".format(time.time() - start))
cp.cuda.Device(1)
dets_gpu = cp.array(dets)
start = time.time()
keep = nms_gpu(dets_gpu, thresh)
print("GPU implementation took: {}".format(time.time() - start))
错误是
CPU implementation took: 0.3672311305999756
Traceback (most recent call last):
File "nms_rotated.py", line 117, in
keep = nms_gpu(dets_gpu, thresh)File "nms_rotated.py", line 97, in nms_gpu
iou = polygon_iou_gpu(polys[i], polys[order[j + 1]])TypeError: list indices must be integers or slices, not cupy.core.core.ndarray
更新：2019 年 2 月 13 日 我尝试了@Yuki Hashimoto 的回答
将 iou = polygon_iou_gpu(polys[i], polys[order[j + 1]]) 替换为 iou = polygon_iou_gpu(polys[i.get()], polys[order[j + 1].get()])。它不会抛出任何错误，但 GPU 版本比 CPU 版本慢数倍。
通过使用 100000 次随机检测：
CPU implementation took: 47.125494956970215 GPU implementation took: 142.08464860916138
[更新 2019/2/13]
请参考@yuki-hashimoto 的回答，比较合适
如错误消息中所写
TypeError: list indices must be integers or slices, not cupy.core.core.ndarray
我猜 order 是 cupy 数组？
在那种情况下，polys[order[j + 1]] 正在使用索引 order[j+1] 作为 cupy 数组，这可能会导致问题。
如何尝试通过 cuda.to_cpu(array) 方法将它们转换为 numpy 数组？
from chainer import cuda
iou = polygon_iou_gpu(polys[i], polys[cuda.to_cpu(order[j + 1])])
简而言之：使用PFN的官方non-maximum suppression。
详情：
使用 cp.where，其中 returns 一个符合某些条件的 list 对象。
不推荐corochann的答案，因为polys是一个列表，list也不应该被np.ndarray分割。 （并且不推荐注入另一个依赖...）
>>> polys[order.get()] # get method returns np.ndarray
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: only integer scalar arrays can be converted to a scalar index
>>> polys[order[j + 1].get()]
### some result in some case, but this may fails depending on your env.###