inference-engine/thirdparty/clDNN/tests/test_cases/propagate_constants_gpu_test.cpp

   1 /*
   2 // Copyright (c) 2018 Intel Corporation
   3 //
   4 // Licensed under the Apache License, Version 2.0 (the "License");
   5 // you may not use this file except in compliance with the License.
   6 // You may obtain a copy of the License at
   7 //
   8 //      http://www.apache.org/licenses/LICENSE-2.0
   9 //
  10 // Unless required by applicable law or agreed to in writing, software
  11 // distributed under the License is distributed on an "AS IS" BASIS,
  12 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 // See the License for the specific language governing permissions and
  14 // limitations under the License.
  15 */
  16
  17 ///////////////////////////////////////////////////////////////////////////////////////////////////
  18
  19 #include <gtest/gtest.h>
  20 #include "api/CPP/memory.hpp"
  21 #include <api/CPP/input_layout.hpp>
  22 #include <api/CPP/topology.hpp>
  23 #include <api/CPP/network.hpp>
  24 #include <api/CPP/engine.hpp>
  25 #include "test_utils/test_utils.h"
  26 #include <api/CPP/concatenation.hpp>
  27 #include <api/CPP/reorder.hpp>
  28 #include <api/CPP/data.hpp>
  29 #include <api/CPP/reshape.hpp>
  30
  31 using namespace cldnn;
  32 using namespace tests;
  33
  34 //We expect additional reorder to be added in between "weights1" and "reshape1".
  35 //This situation should be handled properly by propagate constants optimization phase
  36 TEST(propagate_constants, copy_dependecies_from_nodes) {
  37     const auto& engine = get_test_engine();
  38     build_options build_opt;
  39     build_opt.set_option(build_option::optimize_data(true));
  40
  41     auto input = memory::allocate(engine, { data_types::f16, format::yxfb,{ 1, 1, 2, 2 } });
  42     auto weights1 = memory::allocate(engine, { data_types::f16, format::yxfb,{ 1, 1, 2, 1 } });
  43     auto weights2 = memory::allocate(engine, { data_types::f32, format::byxf,{ 1, 1, 1, 2 } });
  44
  45     set_values(input, { FLOAT16(1.1f), FLOAT16(1.2f), FLOAT16(1.3f), FLOAT16(1.4f) });
  46     set_values(weights1, { FLOAT16(2.1f), FLOAT16(3.1f) });
  47     set_values(weights2, { 1.1f, 0.1f });
  48
  49     topology topology;
  50     topology.add(input_layout("input", input.get_layout()));
  51     topology.add(data("weights1", weights1));
  52     topology.add(data("weights2", weights2));
  53     topology.add(reshape("reshape1", "weights1", tensor(spatial(1, 2))));
  54     topology.add(reorder("reorder2", "input", layout(data_types::f32, format::byxf, 4)));
  55     topology.add(reorder("reorder1", "reshape1", layout(data_types::f32, format::byxf, 4)));
  56     topology.add(concatenation("concat", { "reorder1", "weights2" }, concatenation::along_x));
  57     topology.add(convolution("conv2", { "reorder2" }, { "concat" }));
  58     network network(engine, topology, build_opt);
  59     network.set_input_data("input", input);
  60
  61     auto outputs = network.execute();
  62
  63     float epsilon = 1e-2f;
  64     for (auto& it : outputs)
  65     {
  66         auto output = it.second.get_memory().pointer<float>();
  67         EXPECT_NEAR(7.8f, output[0], epsilon);
  68     }
  69 }