/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. 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 #include #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/math/MathUtils.h" #include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" using namespace paddle; // NOLINT using namespace std; // NOLINT DECLARE_bool(use_gpu); DECLARE_int32(gpu_id); DECLARE_double(checkgrad_eps); DECLARE_bool(thread_local_rand_use_global_seed); DECLARE_bool(prev_batch_state); // Test that the convTrans forward is the same as conv backward TEST(Layer, convTransLayerFwd) { // Setting up conv-trans layer TestConfig configt; configt.biasSize = 3; configt.layerConfig.set_type("exconvt"); configt.layerConfig.set_num_filters(3); configt.layerConfig.set_partial_sum(1); configt.layerConfig.set_shared_biases(true); configt.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 384}); LayerInputConfig* input = configt.layerConfig.add_inputs(); ConvConfig* conv = input->mutable_conv_conf(); conv->set_filter_size(2); conv->set_filter_size_y(4); conv->set_channels(16); conv->set_padding(0); conv->set_padding_y(1); conv->set_stride(2); conv->set_stride_y(2); conv->set_groups(1); conv->set_filter_channels(3 / conv->groups()); conv->set_img_size(16); conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(), conv->padding(), conv->stride(), /* caffeMode */ true)); configt.layerConfig.set_size(conv->img_size() * conv->img_size() * configt.layerConfig.num_filters()); configt.layerConfig.set_name("convTrans"); // data layer initialize std::vector dataLayers; LayerMap layerMap; vector datas; initDataLayer( configt, &dataLayers, &datas, &layerMap, "convTrans", 100, false, false); // test layer initialize std::vector parameters; LayerPtr convtLayer; initTestLayer(configt, &layerMap, ¶meters, &convtLayer); convtLayer->getBiasParameter()->zeroMem(); convtLayer->forward(PASS_GC); // Setting up conv-layer config TestConfig config; config.biasSize = 16; config.layerConfig.set_type("exconv"); config.layerConfig.set_num_filters(16); config.layerConfig.set_partial_sum(1); config.layerConfig.set_shared_biases(true); config.inputDefs.push_back({INPUT_DATA, "layer_1", 768, 384}); input = config.layerConfig.add_inputs(); conv = input->mutable_conv_conf(); conv->set_filter_size(2); conv->set_filter_size_y(4); conv->set_channels(3); conv->set_padding(0); conv->set_padding_y(1); conv->set_stride(2); conv->set_stride_y(2); conv->set_groups(1); conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_img_size(16); conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(), conv->padding(), conv->stride(), /* caffeMode */ true)); config.layerConfig.set_size(conv->output_x() * conv->output_x() * config.layerConfig.num_filters()); config.layerConfig.set_name("conv"); // data layer initialize std::vector dataLayers2; LayerMap layerMap2; vector datas2; initDataLayer( config, &dataLayers2, &datas2, &layerMap2, "conv", 100, false, false); // test layer initialize std::vector parameters2; LayerPtr convLayer; initTestLayer(config, &layerMap2, ¶meters2, &convLayer); // Sync convLayer and convtLayer parameter convLayer->getBiasParameter()->zeroMem(); convLayer->getParameters()[0] ->getBuf(PARAMETER_VALUE) ->copyFrom(*(convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE))); // Set convLayer outputGrad as convTransLayer input value convLayer->forward(PASS_GC); convLayer->getOutput().grad->copyFrom(*(dataLayers[0]->getOutputValue())); vector callbackFlags(parameters2.size(), 0); auto callback = [&](Parameter* para) { ++callbackFlags[para->getID()]; }; convLayer->backward(callback); // Check that the convLayer backward is the same as convTransLayer forward checkMatrixEqual(convtLayer->getOutputValue(), dataLayers2[0]->getOutputGrad()); } // Do one forward pass of convTrans layer and check to see if its output // matches the given result void doOneConvtTest(size_t imgSize, size_t output_x, size_t stride, size_t padding, size_t filter_size, MatrixPtr& result) { TestConfig configt; configt.biasSize = 1; configt.layerConfig.set_type("exconvt"); configt.layerConfig.set_num_filters(1); configt.layerConfig.set_partial_sum(1); configt.layerConfig.set_shared_biases(true); configt.inputDefs.push_back( {INPUT_DATA, "layer_0", output_x * output_x, filter_size * filter_size}); LayerInputConfig* input = configt.layerConfig.add_inputs(); ConvConfig* conv = input->mutable_conv_conf(); conv->set_filter_size(filter_size); conv->set_filter_size_y(filter_size); conv->set_channels(1); conv->set_padding(padding); conv->set_padding_y(padding); conv->set_stride(stride); conv->set_stride_y(stride); conv->set_groups(1); conv->set_filter_channels(1); conv->set_img_size(imgSize); conv->set_output_x(output_x); configt.layerConfig.set_size(conv->img_size() * conv->img_size() * configt.layerConfig.num_filters()); configt.layerConfig.set_name("convTrans"); std::vector dataLayers; LayerMap layerMap; vector datas; initDataLayer( configt, &dataLayers, &datas, &layerMap, "convTrans", 1, false, false); dataLayers[0]->getOutputValue()->zeroMem(); dataLayers[0]->getOutputValue()->add(1.0); // test layer initialize std::vector parameters; LayerPtr convtLayer; initTestLayer(configt, &layerMap, ¶meters, &convtLayer); convtLayer->getBiasParameter()->zeroMem(); convtLayer->getParameters()[0]->zeroMem(); convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->add(1.0); convtLayer->forward(PASS_GC); checkMatrixEqual(convtLayer->getOutputValue(), result); } TEST(Layer, convTransLayerFwd2) { MatrixPtr result; result = Matrix::create(1, 5 * 5, false, false); result->zeroMem(); result->add(1.0); doOneConvtTest(/* imgSize */ 5, /* output_x */ 1, /* stride */ 1, /* padding */ 0, /* filter_size */ 5, result); real resultData[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4, 4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1}; result->setData(resultData); doOneConvtTest(/* imgSize */ 5, /* output_x */ 2, /* stride */ 1, /* padding */ 0, /* filter_size */ 4, result); real resultData2[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4, 4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1}; result->setData(resultData2); doOneConvtTest(/* imgSize */ 5, /* output_x */ 2, /* stride */ 2, /* padding */ 1, /* filter_size */ 5, result); real resultData3[] = {1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 4, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1}; result->setData(resultData3); doOneConvtTest(/* imgSize */ 5, /* output_x */ 2, /* stride */ 2, /* padding */ 0, /* filter_size */ 3, result); } int main(int argc, char** argv) { testing::InitGoogleTest(&argc, argv); initMain(argc, argv); FLAGS_thread_local_rand_use_global_seed = true; srand(1); return RUN_ALL_TESTS(); }