# adaptive max pool3d
# suppose input data in the shape of [N, C, D, H, W], `output_size` is [l, m, n]
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
# of input data into m*n grids averagely and performs poolings in each
# grid to get output.
# adaptive max pool performs calculations as follow:
#
#     for i in range(l):
#         for j in range(m):
#             for k in range(n):
#                 dstart = floor(i * D / l)
#                 dend = ceil((i + 1) * D / l)
#                 hstart = floor(i * H / m)
#                 hend = ceil((i + 1) * H / m)
#                 wstart = floor(i * W / n)
#                 wend = ceil((i + 1) * W / n)
#             output[:, :, i, j, k] = max(input[:, :, dstart: dend, hstart: hend, wstart: wend])
#

import paddle
x = paddle.rand((2, 3, 8, 32, 32))
# x.shape is [2, 3, 8, 32, 32]
out = paddle.nn.functional.adaptive_max_pool3d(
                x = x,
                output_size=[3, 3, 3])
print(out.shape)
# out.shape is [2, 3, 3, 3, 3]