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Diffstat (limited to 'libs/kernel/acl/src/DepthwiseConv2D.test.h')
| -rw-r--r-- | libs/kernel/acl/src/DepthwiseConv2D.test.h | 245 |
1 files changed, 245 insertions, 0 deletions
diff --git a/libs/kernel/acl/src/DepthwiseConv2D.test.h b/libs/kernel/acl/src/DepthwiseConv2D.test.h new file mode 100644 index 000000000..b2c8592ee --- /dev/null +++ b/libs/kernel/acl/src/DepthwiseConv2D.test.h @@ -0,0 +1,245 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed 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. + */ + +#include <gtest/gtest.h> +#include <OperationsUtils.h> +#include <kernel/acl/nnfw_kernel_acl.h> +#include <kernel/acl/DepthwiseConv2D.h> + +// TODO: fix include path in CMakeFiles +#include "util.h" + +#ifndef ACL_TEST +#error "ACL_TEST should be defined first!" +#endif // ACL_TEST + +#ifndef ACL_CORE_FUNC_NAME +#error "ACL_CORE_FUNC_NAME should be defined first!" +#endif // ACL_CORE_FUNC_NAME + +using namespace nnfw::kernel::acl; + +ACL_TEST(KernelACL_TC, dwise_conv2d_1) { + uint32_t input_n = 1; + uint32_t input_h = 3; + uint32_t input_w = 3; + uint32_t input_c = 1; + uint32_t filter_h = 3; + uint32_t filter_w = 3; + uint32_t filter_c = 1; + uint32_t out_h = 1; + uint32_t out_w = 1; + + int32_t padding_left = 0; + int32_t padding_right = 0; + int32_t padding_top = 0; + int32_t padding_bottom = 0; + int32_t stride_width = 1; + int32_t stride_height = 1; + int32_t depth_multiplier = 1; + + util::TensorWrapper input({input_n, input_h, input_w, input_c}); + util::TensorWrapper weights({1, filter_h, filter_w, filter_c}); + util::TensorWrapper bias({filter_c}); + util::TensorWrapper output({1, out_h, out_w, filter_c}); + + int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); + + input.initValue([&](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + uint32_t N = input_n; + uint32_t H = input_h; + uint32_t W = input_w; + uint32_t C = input_c; + + return n*H*W*C + h*W*C + w*C + c; + }); + weights.initValue([&](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + uint32_t N = 1; + uint32_t H = filter_h; + uint32_t W = filter_w; + uint32_t C = filter_c; + + return n*H*W*C + h*W*C + w*C + c; + }); + bias.initValue([](uint32_t w) { + return 0.f; + }); + output.initValue([](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + return 0.f; + }); + + bool bret = ACL_CORE_FUNC_NAME(input.ptr<float>(), input.shape(), + weights.ptr<float>(), weights.shape(), + bias.ptr<float>(), bias.shape(), + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + depth_multiplier, activation, + output.ptr<float>(), output.shape()); + + EXPECT_EQ(bret, true); + + util::TensorWrapper expected({1, out_h, out_w, filter_c}); + expected.initValue([&](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + return 204.f; + }); + + EXPECT_EQ(output, expected); +} + +ACL_TEST(KernelACL_TC, dwise_conv2d_multi_channel) { + uint32_t input_n = 1; + uint32_t input_h = 3; + uint32_t input_w = 3; + uint32_t input_c = 3; + uint32_t filter_h = 3; + uint32_t filter_w = 3; + uint32_t filter_c = input_c; + uint32_t out_h = 1; + uint32_t out_w = 1; + + int32_t padding_left = 0; + int32_t padding_right = 0; + int32_t padding_top = 0; + int32_t padding_bottom = 0; + int32_t stride_width = 1; + int32_t stride_height = 1; + int32_t depth_multiplier = 1; + + util::TensorWrapper input({input_n, input_h, input_w, input_c}); + util::TensorWrapper weights({1, filter_h, filter_w, filter_c}); + util::TensorWrapper bias({filter_c}); + util::TensorWrapper output({1, out_h, out_w, filter_c}); + + int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); + + input.initValue([&](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + uint32_t N = input_n; + uint32_t H = input_h; + uint32_t W = input_w; + uint32_t C = input_c; + + return n*H*W*C + h*W*C + w*C + c; + }); + weights.initValue([&](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + uint32_t N = 1; + uint32_t H = filter_h; + uint32_t W = filter_w; + uint32_t C = filter_c; + + return n*H*W*C + h*W*C + w*C + c; + }); + bias.initValue([](uint32_t w) { + return 0.f; + }); + output.initValue([](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + return 0.f; + }); + + bool bret = ACL_CORE_FUNC_NAME(input.ptr<float>(), input.shape(), + weights.ptr<float>(), weights.shape(), + bias.ptr<float>(), bias.shape(), + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + depth_multiplier, activation, + output.ptr<float>(), output.shape()); + + EXPECT_EQ(bret, true); + + util::TensorWrapper expected({1, out_h, out_w, filter_c}); + expected.initValue({ + 1836.f, + 2061.f, + 2304.f + }); + + EXPECT_EQ(output, expected); +} + +ACL_TEST(KernelACL_TC, dwise_conv2d_inception_1) { + uint32_t input_n = 1; + uint32_t input_h = 112; + uint32_t input_w = 112; + uint32_t input_c = 32; + uint32_t filter_h = 3; + uint32_t filter_w = 3; + uint32_t filter_c = input_c; + uint32_t out_h = 112; + uint32_t out_w = 112; + + int32_t padding_left = 1; + int32_t padding_right = 1; + int32_t padding_top = 1; + int32_t padding_bottom = 1; + int32_t stride_width = 1; + int32_t stride_height = 1; + int32_t depth_multiplier = 1; + + util::TensorWrapper input({input_n, input_h, input_w, input_c}); + util::TensorWrapper weights({1, filter_h, filter_w, filter_c}); + util::TensorWrapper bias({filter_c}); + util::TensorWrapper output({1, out_h, out_w, filter_c}); + + int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU6); + + input.initValue([](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + return c; + }); + weights.initValue([](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + return c; + }); + bias.initValue([](uint32_t w) { + return 0.f; + }); + output.initValue([](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + return 0.f; + }); + + bool bret = ACL_CORE_FUNC_NAME(input.ptr<float>(), input.shape(), + weights.ptr<float>(), weights.shape(), + bias.ptr<float>(), bias.shape(), + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + depth_multiplier, activation, + output.ptr<float>(), output.shape()); + + EXPECT_EQ(bret, true); + + util::TensorWrapper expected({1, out_h, out_w, filter_c}); + expected.initValue([&](uint32_t n, uint32_t c, uint32_t h, uint32_t w) { + float v = 9.f; + if( h == 0 || h == out_h-1 ) + v -= 3.f; + if( w == 0 || w == out_w-1 ) + v -= 3.f; + + // four corners + if( (w == 0 && h == 0) + || (w == 0 && h == out_h-1) + || (w == out_w-1 && h == 0) + || (w == out_w-1 && h == out_h-1) ) + v += 1.f; + + // Assumption: negative numbers cannot appear because + // only positive numbers exist in the input and weights. + float ret = c*c*v; + return std::min(ret, 6.f); + }); + + EXPECT_EQ(output, expected); +} |