Testing AvgPooling with count_include_pad

avgpooling_test.py

import numpy as np
import tvm
import topi
import torch
from topi.util import get_const_tuple

def verify_pool(n, ic, ih, kh, sh, padding, ceil_mode, count_include_pad=True):
    iw = ih
    kw = kh
    sw = sh
    A = tvm.placeholder((n, ic, ih, iw), name='A')
    B = topi.nn.pool(A, kernel=[kh, kw], stride=[sh, sw], padding=padding,
                     pool_type='avg', ceil_mode=ceil_mode, count_include_pad=count_include_pad)
    B = topi.nn.relu(B)
    dtype = A.dtype

    a_np = np.random.uniform(size=(n, ic, ih, iw)).astype(dtype)

    avg_pool = torch.nn.AvgPool2d(kernel_size=(kh, kh), stride=(sh, sh), padding=padding, ceil_mode=ceil_mode,
                                  count_include_pad=count_include_pad)
    b_torch_np = avg_pool(torch.Tensor(a_np)).numpy()

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            s = topi.generic.schedule_pool(B)

        a = tvm.nd.array(a_np, ctx)
        b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
        f = tvm.build(s, [A, B], device)
        f(a, b)
        np.testing.assert_allclose(b.asnumpy(), b_torch_np, rtol=1e-5)

    for device in ['cuda', 'llvm']:
        check_device(device)

def test_pool():
    verify_pool(1, 256, 32, 2, 2, [0, 0], False, True)
    verify_pool(1, 256, 31, 4, 4, [1, 2], False, True)
    verify_pool(1, 256, 32, 4, 4, [1, 2], False, False)
    verify_pool(1, 256, 31, 6, 6, [3, 3], False, False)
    verify_pool(1, 256, 31, 6, 6, [0, 0], False, False)

if __name__ == "__main__":
    test_pool()