// 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<DataLayerPtr> dataLayers; LayerMap layerMap; vector<Argument> 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<ParameterPtr> 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 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<DataLayerPtr> dataLayers; LayerMap layerMap; vector<Argument> datas; initDataLayer(configt, &dataLayers, &datas, &layerMap, "convTrans", 100, false, false); // test layer initialize std::vector<ParameterPtr> 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<DataLayerPtr> dataLayers2; LayerMap layerMap2; vector<Argument> datas2; initDataLayer(config, &dataLayers2, &datas2, &layerMap2, "conv", 100, false, false); // test layer initialize std::vector<ParameterPtr> 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<int> 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()); }
static void getMKLDNNConvConfig(TestConfig& cfg, const testConvDesc& pm) { cfg.layerConfig.set_type("mkldnn_conv"); cfg.layerConfig.set_active_type("relu"); cfg.layerConfig.set_num_filters(pm.oc); cfg.layerConfig.set_size(pm.oc * pm.oh * pm.ow); cfg.layerConfig.set_shared_biases(true); cfg.inputDefs.push_back( {INPUT_DATA, "layer_0", /* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw), /* size of weight= */ size_t(pm.oc * pm.ic * pm.fh * pm.fw / pm.gp)}); LayerInputConfig* input = cfg.layerConfig.add_inputs(); ConvConfig* conv = input->mutable_conv_conf(); conv->set_groups(pm.gp); conv->set_img_size(pm.iw); conv->set_img_size_y(pm.ih); conv->set_output_x(pm.ow); conv->set_output_y(pm.oh); conv->set_filter_size(pm.fw); conv->set_filter_size_y(pm.fh); conv->set_channels(pm.ic); conv->set_padding(pm.pw); conv->set_padding_y(pm.ph); conv->set_stride(pm.sw); conv->set_stride_y(pm.sh); conv->set_dilation(pm.dw); conv->set_dilation_y(pm.dh); conv->set_caffe_mode(true); conv->set_filter_channels(conv->channels() / conv->groups()); CHECK_EQ(conv->filter_channels() * pm.gp, conv->channels()) << "it is indivisible"; int fh = (pm.fh - 1) * pm.dh + 1; int fw = (pm.fw - 1) * pm.dw + 1; int ow = outputSize(pm.iw, fw, pm.pw, pm.sw, true); int oh = outputSize(pm.ih, fh, pm.ph, pm.sh, true); CHECK_EQ(ow, pm.ow) << "output size check failed"; CHECK_EQ(oh, pm.oh) << "output size check failed"; }