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Diffstat (limited to 'compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp')
| -rw-r--r-- | compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp | 164 |
1 files changed, 164 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp b/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp new file mode 100644 index 000000000..48824c2e0 --- /dev/null +++ b/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp @@ -0,0 +1,164 @@ +/* + * Copyright (c) 2019 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 "NodeExecution.h" + +#include "locomotiv/NodeData.h" +#include "NodeDataImpl.h" +#include "NodeDomain.h" + +#include <nncc/core/ADT/tensor/Shape.h> +#include <nncc/core/ADT/tensor/Buffer.h> +#include <nncc/core/ADT/tensor/Overlay.h> +#include <nncc/core/ADT/tensor/LexicalLayout.h> +#include "nncc/core/ADT/tensor/IndexEnumerator.h" + +#include <gtest/gtest.h> + +namespace +{ +using nncc::core::ADT::tensor::Shape; +using nncc::core::ADT::tensor::LexicalLayout; +using nncc::core::ADT::tensor::make_buffer; +using nncc::core::ADT::tensor::make_overlay; + +void run_test(const float *ifm, const float *ker, const float *expected_ofm, const Shape &ifm_shape, + const Shape ker_shape, const Shape ofm_shape, const uint32_t stride_v, + const uint32_t stride_h, const uint32_t pad_top = 0, const uint32_t pad_bottom = 0, + const uint32_t pad_left = 0, const uint32_t pad_right = 0) +{ + auto g = loco::make_graph(); + + // Fill output data of FeatureEncode from ifm + auto ifm_enc = g->nodes()->create<loco::FeatureEncode>(); + { + auto ifm_enc_buf = make_buffer<float, LexicalLayout>(ifm_shape); + auto ifm_overlay = make_overlay<float, LexicalLayout>(ifm_shape, const_cast<float *>(ifm)); + for (nncc::core::ADT::tensor::IndexEnumerator e{ifm_shape}; e.valid(); e.advance()) + { + const auto &ind = e.current(); + ifm_enc_buf.at(ind) = ifm_overlay.at(ind); + } + + auto enc_data = locomotiv::make_data(ifm_enc_buf); + locomotiv::annot_data(ifm_enc, std::move(enc_data)); + locomotiv::annot_domain(ifm_enc, loco::Domain::Feature); + } + + // Fill output data of DepthwiseFilterEncode from ker + auto ker_enc = g->nodes()->create<loco::DepthwiseFilterEncode>(); + { + auto ker_enc_buf = make_buffer<float, LexicalLayout>(ker_shape); + auto ker_overlay = make_overlay<float, LexicalLayout>(ker_shape, const_cast<float *>(ker)); + for (nncc::core::ADT::tensor::IndexEnumerator e{ker_shape}; e.valid(); e.advance()) + { + const auto &ind = e.current(); + ker_enc_buf.at(ind) = ker_overlay.at(ind); + } + + auto enc_data = locomotiv::make_data(ker_enc_buf); + locomotiv::annot_data(ker_enc, std::move(enc_data)); + locomotiv::annot_domain(ker_enc, loco::Domain::DepthwiseFilter); + } + + // build DepthwiseConv2D + auto dw_conv2d = g->nodes()->create<loco::DepthwiseConv2D>(); + dw_conv2d->ifm(ifm_enc); + dw_conv2d->ker(ker_enc); + dw_conv2d->stride()->vertical(stride_v); + dw_conv2d->stride()->horizontal(stride_h); + dw_conv2d->pad()->top(pad_top); + dw_conv2d->pad()->bottom(pad_bottom); + dw_conv2d->pad()->left(pad_left); + dw_conv2d->pad()->right(pad_right); + + // run interpreter + locomotiv::NodeExecution::get().run(dw_conv2d); + + // get result of calculation + auto dw_conv2d_result = locomotiv::annot_data(dw_conv2d); + + // check the result + ASSERT_NE(dw_conv2d_result, nullptr); + ASSERT_TRUE(dw_conv2d_result->dtype() == loco::DataType::FLOAT32); + ASSERT_TRUE(*(dw_conv2d_result->shape()) == ofm_shape); + + auto ofm_overlay = + make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); + for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) + { + const auto &ind = e.current(); + ASSERT_FLOAT_EQ(dw_conv2d_result->as_f32_bufptr()->at(ind), ofm_overlay.at(ind)); + } + + ASSERT_EQ(locomotiv::annot_domain(dw_conv2d), loco::Domain::Feature); +} + +} // namespace + +// clang-format off + +/* ifm, ker and ofm are from the code below: + +ifm = tf.random_normal([1, 5, 5, 2], stddev=1.1) +ker = tf.random_normal([4, 4, 2, 3], stddev=1.1) +out = tf.nn.depthwise_conv2d(ifm, ker, strides = [1, 1, 1, 1], padding= 'VALID') + +with tf.Session() as sess: + print(sess.run(out)) +*/ +TEST(NodeExecution_DepthwiseConv2D, f32_random_valid) +{ + using nncc::core::ADT::tensor::Shape; + + const float ifm[] = {0.8122538, 1.209147, 0.6903842, -0.26646265, 1.516799, -1.8540707, + -0.74240327, 1.7811562, -0.03699546, -0.44468504, -1.4982721, -1.1858582, + -0.21140318, -0.974522, 1.0000849, -1.294535, -0.6108882, 0.25827602, + 1.3631831, -0.5180266, 0.20870179, 0.18333802, -0.42263857, -1.6694735, + 0.0415236, -0.3903758, 2.0933757, -0.29660916, 2.1218338, -1.1599928, + 0.57163256, 0.48865932, -1.3622656, 0.35924262, 1.2951899, -0.1769997, + 0.74513537, -0.31920406, -1.2902768, -0.7095059, 1.9157801, -0.41028237, + 1.2502829, 0.3354887, 1.4199319, -0.20366786, -0.8828556, 0.5173567, + 1.7708117, -0.30096334}; + const float ker[] = { + -0.19805557, 0.58464956, -0.7804337, 0.06974592, 0.45790604, 0.24833807, 0.43393376, + 0.2541043, -0.04406675, -0.32167575, 1.0546446, -1.4978354, 0.20829494, 1.1659569, + 0.37908667, -0.94137955, 0.293349, -1.1023049, 0.76133233, 0.55595005, 1.4458209, + 1.6128604, 1.5655615, -2.183877, -0.90535915, -0.49858555, 1.7168728, -1.1590382, + 0.6706056, 1.2215618, -0.06603386, 0.16559464, 0.541991, -0.44488335, 0.766181, + 1.0227629, -0.6352362, -1.670828, -0.63334507, 0.0313305, -0.6721083, 0.50112915, + -0.15218066, 0.67222077, -0.3613627, -0.08516614, -0.5024078, -0.9503976, -2.1892295, + 1.8308185, -0.15187284, 1.5761136, 0.24869336, -1.7378871, -0.22518761, 1.0175673, + 0.7084485, -0.74157554, -1.8185995, -1.3330095, -0.04427439, 1.0556892, -0.68243974, + 0.32001218, 2.0901792, -1.1612813, 0.7294674, 0.05740008, -0.00832882, 1.0446658, + 0.4477195, -0.09174404, -1.0176039, 1.5066665, -2.148343, 0.29421416, 0.93011874, + -0.15737922, -1.6444012, 0.25780794, -0.6545867, -0.3488956, 0.26167992, -0.154414, + 0.2798124, -0.8590068, 2.0494444, 0.48268002, 0.81941164, -0.4848027, 0.76870304, + 0.7102261, 0.45778143, 0.23214905, -0.17742023, -0.75016516}; + const float ofm[] = {4.474646, 0.6792067, -1.9799856, 7.484751, 4.3087378, -1.905938, + 1.4887369, 0.4361322, 0.79539883, -3.8583446, -4.502204, 4.356392, + -5.3030324, 3.493003, -4.349277, 2.3069482, -3.8881323, -0.73901534, + -0.6629516, 2.1247253, -4.9229584, 1.6716996, -3.0208125, 1.0597891}; + + run_test(ifm, ker, ofm, + Shape{1, 5, 5, 2}, Shape{4, 4, 2, 3}, Shape{1, 2, 2, 6}, // shapes of input, ker, output + 1, 1 // stride + ); +} + +// TODO Add same padding test + +// clang-format on |