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127 lines
4.1 KiB
127 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <gtest/gtest.h>
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#include <string>
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#include <vector>
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#include "ModelConfig.pb.h"
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#include "paddle/gserver/layers/DataLayer.h"
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#include "paddle/trainer/Trainer.h"
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#include "paddle/utils/GlobalConstants.h"
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#include "LayerGradUtil.h"
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#include "paddle/testing/TestUtil.h"
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using namespace paddle; // NOLINT
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using namespace std; // NOLINT
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DECLARE_bool(use_gpu);
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DECLARE_int32(gpu_id);
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DECLARE_double(checkgrad_eps);
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DECLARE_bool(thread_local_rand_use_global_seed);
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DECLARE_bool(prev_batch_state);
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// Test that the batchNormLayer can be followed by a ConvLayer
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TEST(Layer, batchNorm) {
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FLAGS_use_gpu = false;
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TestConfig configBN;
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const int CHANNELS = 6272;
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const int IMG_SIZE = 1;
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configBN.layerConfig.set_type("batch_norm");
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configBN.layerConfig.set_name("bn");
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configBN.layerConfig.set_size(CHANNELS * IMG_SIZE * IMG_SIZE);
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configBN.layerConfig.set_active_type("relu");
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configBN.biasSize = CHANNELS;
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configBN.inputDefs.push_back({INPUT_DATA,
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"layer_0",
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/* dim= */ IMG_SIZE * IMG_SIZE * CHANNELS,
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/* paraSize= */ CHANNELS});
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configBN.inputDefs.push_back(
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{INPUT_DATA, "layer_1_running_mean", 1, CHANNELS});
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configBN.inputDefs.back().isStatic = true;
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configBN.inputDefs.push_back(
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{INPUT_DATA, "layer_2_running_var", 1, CHANNELS});
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configBN.inputDefs.back().isStatic = true;
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LayerInputConfig* input = configBN.layerConfig.add_inputs();
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configBN.layerConfig.add_inputs();
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configBN.layerConfig.add_inputs();
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ImageConfig* img_conf = input->mutable_image_conf();
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img_conf->set_channels(CHANNELS);
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img_conf->set_img_size(IMG_SIZE);
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// Setting up conv-layer config
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TestConfig config;
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config.biasSize = 64;
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config.layerConfig.set_type("exconv");
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config.layerConfig.set_num_filters(64);
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config.layerConfig.set_partial_sum(1);
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config.layerConfig.set_shared_biases(true);
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config.inputDefs.push_back({INPUT_DATA, "bn", 6272, 204800});
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input = config.layerConfig.add_inputs();
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ConvConfig* conv = input->mutable_conv_conf();
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conv->set_filter_size(5);
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conv->set_filter_size_y(5);
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conv->set_channels(128);
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conv->set_padding(1);
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conv->set_padding_y(1);
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conv->set_stride(2);
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conv->set_stride_y(2);
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conv->set_groups(1);
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conv->set_filter_channels(conv->channels() / conv->groups());
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conv->set_img_size(7);
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conv->set_output_x(3);
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config.layerConfig.set_size(conv->output_x() * conv->output_x() *
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config.layerConfig.num_filters());
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config.layerConfig.set_name("conv");
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// data layer initialize
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std::vector<DataLayerPtr> dataLayers;
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LayerMap layerMap;
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vector<Argument> datas;
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initDataLayer(configBN,
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&dataLayers,
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&datas,
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&layerMap,
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"batch_norm",
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100,
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false,
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false);
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// test layer initialize
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std::vector<ParameterPtr> parameters;
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LayerPtr bnLayer;
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initTestLayer(configBN, &layerMap, ¶meters, &bnLayer);
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std::vector<ParameterPtr> parameters2;
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LayerPtr convLayer;
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initTestLayer(config, &layerMap, ¶meters2, &convLayer);
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bnLayer->forward(PASS_GC);
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convLayer->forward(PASS_GC);
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CHECK_EQ(static_cast<int>(convLayer->getOutputValue()->getHeight()), 100);
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CHECK_EQ(static_cast<int>(convLayer->getOutputValue()->getWidth()), 576);
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}
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int main(int argc, char** argv) {
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testing::InitGoogleTest(&argc, argv);
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initMain(argc, argv);
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FLAGS_thread_local_rand_use_global_seed = true;
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srand(1);
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return RUN_ALL_TESTS();
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}
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