import itertools
import numpy as np
from megengine import Parameter, tensor
from megengine.module import AvgPool2d, MaxPool2d
def test_avg_pool2d():
def test_func(
batch_size,
in_channels,
out_channels,
in_height,
in_width,
kernel_size,
stride,
padding,
):
pool = AvgPool2d(kernel_size, stride=stride, padding=padding, mode="average")
inp = np.random.normal(
size=(batch_size, in_channels, in_height, in_width)
).astype(np.float32)
out_height = (in_height + padding * 2 - kernel_size) // stride + 1
out_width = (in_width + padding * 2 - kernel_size) // stride + 1
out = pool(tensor(inp))
inp = np.pad(inp, ((0, 0), (0, 0), (padding, padding), (padding, padding)))
expected = np.zeros(
(batch_size, out_channels, out_height, out_width), dtype=np.float32,
)
for n, c, oh, ow in itertools.product(
*map(range, [batch_size, out_channels, out_height, out_width])
):
ih, iw = oh * stride, ow * stride
expected[n, c, oh, ow] = np.sum(
inp[n, c, ih : ih + kernel_size, iw : iw + kernel_size,]
) / (kernel_size * kernel_size)
np.testing.assert_almost_equal(out.numpy(), expected, 1e-5)
test_func(10, 4, 4, 5, 5, 2, 2, 1)
test_func(10, 4, 4, 6, 6, 2, 2, 0)
test_func(10, 16, 16, 14, 14, 2, 2, 0)