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# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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"""test of correlation operator in NCHW layout"""
import sys
import numpy as np
import pytest
import tvm
import tvm.testing
import tvm.topi.testing
from tvm import autotvm, te, topi
_correlation_implement = {
"generic": (topi.nn.correlation_nchw, topi.generic.schedule_correlation_nchw),
"gpu": (topi.cuda.correlation_nchw, topi.cuda.schedule_correlation_nchw),
}
(
data_shape,
kernel_size,
max_displacement,
stride1,
stride2,
pad_size,
is_multiply,
) = tvm.testing.parameters(
((1, 3, 10, 10), 1, 4, 1, 1, 4, True),
((1, 3, 10, 10), 1, 5, 1, 1, 5, True),
((5, 1, 4, 4), 3, 1, 2, 1, 2, True),
((5, 1, 6, 4), 3, 1, 2, 2, 2, False),
((5, 1, 11, 11), 5, 1, 1, 1, 2, False),
)
dtype = tvm.testing.parameter("float32")
@tvm.testing.fixture(cache_return_value=True)
def ref_data(
dtype, data_shape, kernel_size, max_displacement, stride1, stride2, pad_size, is_multiply
):
a_np = np.random.uniform(size=data_shape).astype(dtype)
b_np = np.random.uniform(size=data_shape).astype(dtype)
c_np = tvm.topi.testing.correlation_nchw_python(
a_np, b_np, kernel_size, max_displacement, stride1, stride2, pad_size, is_multiply
)
return a_np, b_np, c_np
def test_correlation_nchw(
target,
dev,
ref_data,
dtype,
kernel_size,
max_displacement,
stride1,
stride2,
pad_size,
is_multiply,
):
a_np, b_np, c_np = ref_data
A = te.placeholder(a_np.shape, name="data1", dtype=dtype)
B = te.placeholder(b_np.shape, name="data2", dtype=dtype)
fcompute, fschedule = tvm.topi.testing.dispatch(target, _correlation_implement)
with tvm.target.Target(target):
C = fcompute(A, B, kernel_size, max_displacement, stride1, stride2, pad_size, is_multiply)
s = fschedule([C])
a = tvm.nd.array(a_np, dev)
b = tvm.nd.array(b_np, dev)
c = tvm.nd.empty(c_np.shape, dtype=dtype, device=dev)
func = tvm.build(s, [A, B, C], target)
func(a, b, c)
tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
if __name__ == "__main__":
tvm.testing.main()