| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License |
| """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() |