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wang-xinyu / tensorrtx

Implementation of popular deep learning networks with TensorRT network definition API
MIT License
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scaled-yolov4 engine build,but got error #311

Closed Adarine99 closed 3 years ago

Adarine99 commented 3 years ago

scaled-yolov4 link:

createEngine as follows: `ICudaEngine createEngine(unsigned int maxBatchSize, IBuilder builder, IBuilderConfig config, DataType dt) { INetworkDefinition network = builder->createNetworkV2(0U);

// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
assert(data);

std::map<std::string, Weights> weightMap = loadWeights("../scaled_yolov4.wts");
Weights emptywts{DataType::kFLOAT, nullptr, 0};

// define each layer.
auto l0 = convBnMish(network, weightMap, *data, 32, 3, 1, 1, 0);
auto l1 = convBnMish(network, weightMap, *l0->getOutput(0), 64, 3, 2, 1, 1);
auto l2 = convBnMish(network, weightMap, *l1->getOutput(0), 64, 1, 1, 0, 2);
auto l3 = convBnMish(network, weightMap, *l2->getOutput(0), 64, 3, 1, 1, 1);
auto ew4 = network->addElementWise(*l3->getOutput(0), *l1->getOutput(0), ElementWiseOperation::kSUM);

auto l5 = convBnMish(network, weightMap, *ew4->getOutput(0), 128, 3, 2, 1, 5);
auto l6 = convBnMish(network, weightMap, *l5->getOutput(0), 64, 1, 1, 0, 6);
auto l7 = l5;
auto l8 = convBnMish(network, weightMap, *l7->getOutput(0), 64, 1, 1, 0, 8);
auto l9 = convBnMish(network, weightMap, *l8->getOutput(0), 64, 1, 1, 0, 9);
auto l10 = convBnMish(network, weightMap, *l9->getOutput(0), 64, 3, 1, 1, 10);
auto ew11 = network->addElementWise(*l10->getOutput(0), *l8->getOutput(0), ElementWiseOperation::kSUM);
auto l12 = convBnMish(network, weightMap, *ew11->getOutput(0), 64, 1, 1, 0, 12);
auto l13 = convBnMish(network, weightMap, *l12->getOutput(0), 64, 3, 1, 1, 13);
auto ew14 = network->addElementWise(*l13->getOutput(0), *ew11->getOutput(0), ElementWiseOperation::kSUM);
auto l15 = convBnMish(network, weightMap, *ew14->getOutput(0), 64, 1, 1, 0, 15);

ITensor* inputTensors16[] = {l15->getOutput(0), l6->getOutput(0)};
auto cat16 = network->addConcatenation(inputTensors16, 2);

auto l17 = convBnMish(network, weightMap, *cat16->getOutput(0), 128, 1, 1, 0, 17);
auto l18 = convBnMish(network, weightMap, *l17->getOutput(0), 256, 3, 2, 1, 18);
auto l19 = convBnMish(network, weightMap, *l18->getOutput(0), 128, 1, 1, 0, 19);
auto l20 = l18;
auto l21 = convBnMish(network, weightMap, *l20->getOutput(0), 128, 1, 1, 0, 21);
auto l22 = convBnMish(network, weightMap, *l21->getOutput(0), 128, 1, 1, 0, 22);
auto l23 = convBnMish(network, weightMap, *l22->getOutput(0), 128, 3, 1, 1, 23);
auto ew24 = network->addElementWise(*l23->getOutput(0), *l21->getOutput(0), ElementWiseOperation::kSUM);
auto l25 = convBnMish(network, weightMap, *ew24->getOutput(0), 128, 1, 1, 0, 25);
auto l26 = convBnMish(network, weightMap, *l25->getOutput(0), 128, 3, 1, 1, 26);
auto ew27 = network->addElementWise(*l26->getOutput(0), *ew24->getOutput(0), ElementWiseOperation::kSUM);
auto l28 = convBnMish(network, weightMap, *ew27->getOutput(0), 128, 1, 1, 0, 28);
auto l29 = convBnMish(network, weightMap, *l28->getOutput(0), 128, 3, 1, 1, 29);
auto ew30 = network->addElementWise(*l29->getOutput(0), *ew27->getOutput(0), ElementWiseOperation::kSUM);
auto l31 = convBnMish(network, weightMap, *ew30->getOutput(0), 128, 1, 1, 0, 31);
auto l32 = convBnMish(network, weightMap, *l31->getOutput(0), 128, 3, 1, 1, 32);
auto ew33 = network->addElementWise(*l32->getOutput(0), *ew30->getOutput(0), ElementWiseOperation::kSUM);
auto l34 = convBnMish(network, weightMap, *ew33->getOutput(0), 128, 1, 1, 0, 34);
auto l35 = convBnMish(network, weightMap, *l34->getOutput(0), 128, 3, 1, 1, 35);
auto ew36 = network->addElementWise(*l35->getOutput(0), *ew33->getOutput(0), ElementWiseOperation::kSUM);
auto l37 = convBnMish(network, weightMap, *ew36->getOutput(0), 128, 1, 1, 0, 37);
auto l38 = convBnMish(network, weightMap, *l37->getOutput(0), 128, 3, 1, 1, 38);
auto ew39 = network->addElementWise(*l38->getOutput(0), *ew36->getOutput(0), ElementWiseOperation::kSUM);
auto l40 = convBnMish(network, weightMap, *ew39->getOutput(0), 128, 1, 1, 0, 40);
auto l41 = convBnMish(network, weightMap, *l40->getOutput(0), 128, 3, 1, 1, 41);
auto ew42 = network->addElementWise(*l41->getOutput(0), *ew39->getOutput(0), ElementWiseOperation::kSUM);
auto l43 = convBnMish(network, weightMap, *ew42->getOutput(0), 128, 1, 1, 0, 43);
auto l44 = convBnMish(network, weightMap, *l43->getOutput(0), 128, 3, 1, 1, 44);
auto ew45 = network->addElementWise(*l44->getOutput(0), *ew42->getOutput(0), ElementWiseOperation::kSUM);
auto l46 = convBnMish(network, weightMap, *ew45->getOutput(0), 128, 1, 1, 0, 46);

ITensor* inputTensors47[] = {l46->getOutput(0), l19->getOutput(0)};
auto cat47 = network->addConcatenation(inputTensors47, 2);

auto l48 = convBnMish(network, weightMap, *cat47->getOutput(0), 256, 1, 1, 0, 48);
auto l49 = convBnMish(network, weightMap, *l48->getOutput(0), 512, 3, 2, 1, 49);
auto l50 = convBnMish(network, weightMap, *l49->getOutput(0), 256, 1, 1, 0, 50);
auto l51 = l49;
auto l52 = convBnMish(network, weightMap, *l51->getOutput(0), 256, 1, 1, 0, 52);
auto l53 = convBnMish(network, weightMap, *l52->getOutput(0), 256, 1, 1, 0, 53);
auto l54 = convBnMish(network, weightMap, *l53->getOutput(0), 256, 3, 1, 1, 54);
auto ew55 = network->addElementWise(*l54->getOutput(0), *l52->getOutput(0), ElementWiseOperation::kSUM);
auto l56 = convBnMish(network, weightMap, *ew55->getOutput(0), 256, 1, 1, 0, 56);
auto l57 = convBnMish(network, weightMap, *l56->getOutput(0), 256, 3, 1, 1, 57);
auto ew58 = network->addElementWise(*l57->getOutput(0), *ew55->getOutput(0), ElementWiseOperation::kSUM);
auto l59 = convBnMish(network, weightMap, *ew58->getOutput(0), 256, 1, 1, 0, 59);
auto l60 = convBnMish(network, weightMap, *l59->getOutput(0), 256, 3, 1, 1, 60);
auto ew61 = network->addElementWise(*l60->getOutput(0), *ew58->getOutput(0), ElementWiseOperation::kSUM);
auto l62 = convBnMish(network, weightMap, *ew61->getOutput(0), 256, 1, 1, 0, 62);
auto l63 = convBnMish(network, weightMap, *l62->getOutput(0), 256, 3, 1, 1, 63);
auto ew64 = network->addElementWise(*l63->getOutput(0), *ew61->getOutput(0), ElementWiseOperation::kSUM);
auto l65 = convBnMish(network, weightMap, *ew64->getOutput(0), 256, 1, 1, 0, 65);
auto l66 = convBnMish(network, weightMap, *l65->getOutput(0), 256, 3, 1, 1, 66);
auto ew67 = network->addElementWise(*l66->getOutput(0), *ew64->getOutput(0), ElementWiseOperation::kSUM);
auto l68 = convBnMish(network, weightMap, *ew67->getOutput(0), 256, 1, 1, 0, 68);
auto l69 = convBnMish(network, weightMap, *l68->getOutput(0), 256, 3, 1, 1, 69);
auto ew70 = network->addElementWise(*l69->getOutput(0), *ew67->getOutput(0), ElementWiseOperation::kSUM);
auto l71 = convBnMish(network, weightMap, *ew70->getOutput(0), 256, 1, 1, 0, 71);
auto l72 = convBnMish(network, weightMap, *l71->getOutput(0), 256, 3, 1, 1, 72);
auto ew73 = network->addElementWise(*l72->getOutput(0), *ew70->getOutput(0), ElementWiseOperation::kSUM);
auto l74 = convBnMish(network, weightMap, *ew73->getOutput(0), 256, 1, 1, 0, 74);
auto l75 = convBnMish(network, weightMap, *l74->getOutput(0), 256, 3, 1, 1, 75);
auto ew76 = network->addElementWise(*l75->getOutput(0), *ew73->getOutput(0), ElementWiseOperation::kSUM);
auto l77 = convBnMish(network, weightMap, *ew76->getOutput(0), 256, 1, 1, 0, 77);

ITensor* inputTensors78[] = {l77->getOutput(0), l50->getOutput(0)};
auto cat78 = network->addConcatenation(inputTensors78, 2);

auto l79 = convBnMish(network, weightMap, *cat78->getOutput(0), 512, 1, 1, 0, 79);
auto l80 = convBnMish(network, weightMap, *l79->getOutput(0), 1024, 3, 2, 1, 80);
auto l81 = convBnMish(network, weightMap, *l80->getOutput(0), 512, 1, 1, 0, 81);
auto l82 = l80;
auto l83 = convBnMish(network, weightMap, *l82->getOutput(0), 512, 1, 1, 0, 83);
auto l84 = convBnMish(network, weightMap, *l83->getOutput(0), 512, 1, 1, 0, 84);
auto l85 = convBnMish(network, weightMap, *l84->getOutput(0), 512, 3, 1, 1, 85);
auto ew86 = network->addElementWise(*l85->getOutput(0), *l83->getOutput(0), ElementWiseOperation::kSUM);
auto l87 = convBnMish(network, weightMap, *ew86->getOutput(0), 512, 1, 1, 0, 87);
auto l88 = convBnMish(network, weightMap, *l87->getOutput(0), 512, 3, 1, 1, 88);
auto ew89 = network->addElementWise(*l88->getOutput(0), *ew86->getOutput(0), ElementWiseOperation::kSUM);
auto l90 = convBnMish(network, weightMap, *ew89->getOutput(0), 512, 1, 1, 0, 90);
auto l91 = convBnMish(network, weightMap, *l90->getOutput(0), 512, 3, 1, 1, 91);
auto ew92 = network->addElementWise(*l91->getOutput(0), *ew89->getOutput(0), ElementWiseOperation::kSUM);
auto l93 = convBnMish(network, weightMap, *ew92->getOutput(0), 512, 1, 1, 0, 93);
auto l94 = convBnMish(network, weightMap, *l93->getOutput(0), 512, 3, 1, 1, 94);
auto ew95 = network->addElementWise(*l94->getOutput(0), *ew92->getOutput(0), ElementWiseOperation::kSUM);
auto l96 = convBnMish(network, weightMap, *ew95->getOutput(0), 512, 1, 1, 0, 96);

ITensor* inputTensors97[] = {l96->getOutput(0), l81->getOutput(0)};
auto cat97 = network->addConcatenation(inputTensors97, 2);

auto l98 = convBnMish(network, weightMap, *cat97->getOutput(0), 1024, 1, 1, 0, 98);
auto l99 = convBnMish(network, weightMap, *l98->getOutput(0), 512, 1, 1, 0, 99);
auto l100 = l98;
auto l101 = convBnMish(network, weightMap, *l100->getOutput(0), 512, 1, 1, 0, 101);
auto l102 = convBnMish(network, weightMap, *l101->getOutput(0), 512, 3, 1, 1, 102);
auto l103 = convBnMish(network, weightMap, *l102->getOutput(0), 512, 1, 1, 0, 103);

auto pool104 = network->addPoolingNd(*l103->getOutput(0), PoolingType::kMAX, DimsHW{5, 5});
pool104->setPaddingNd(DimsHW{2, 2});
pool104->setStrideNd(DimsHW{1, 1});

auto l105=l103;

auto pool106 = network->addPoolingNd(*l105->getOutput(0), PoolingType::kMAX, DimsHW{9, 9});
pool106->setPaddingNd(DimsHW{4, 4});
pool106->setStrideNd(DimsHW{1, 1});

auto l107=l103;

auto pool108 = network->addPoolingNd(*l107->getOutput(0), PoolingType::kMAX, DimsHW{13, 13});
pool108->setPaddingNd(DimsHW{6, 6});
pool108->setStrideNd(DimsHW{1, 1}); 
ITensor* inputTensors109[] = {pool108->getOutput(0), pool106->getOutput(0), pool104->getOutput(0), l103->getOutput(0)};
auto cat109 = network->addConcatenation(inputTensors109, 4);

auto l110 = convBnMish(network, weightMap, *cat109->getOutput(0), 512, 1, 1, 0, 110);
auto l111 = convBnMish(network, weightMap, *l110->getOutput(0), 512, 3, 1, 1, 111);
ITensor* inputTensors112[] = {l111->getOutput(0), l99->getOutput(0)};
auto cat112 = network->addConcatenation(inputTensors112, 2);
auto l113 = convBnMish(network, weightMap, *cat112->getOutput(0), 512, 1, 1, 0, 113);
auto l114 = convBnMish(network, weightMap, *l113->getOutput(0), 256, 1, 1, 0, 114);

float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 256 * 2 * 2));
for (int i = 0; i < 256 * 2 * 2; i++) {
    deval[i] = 1.0;
}
Weights deconvwts115{DataType::kFLOAT, deval, 256 * 2 * 2};
IDeconvolutionLayer* deconv115 = network->addDeconvolutionNd(*l114->getOutput(0), 256, DimsHW{2, 2}, deconvwts115, emptywts);
assert(deconv115);
deconv115->setStrideNd(DimsHW{2, 2});
deconv115->setNbGroups(256);
weightMap["deconv115"] = deconvwts115;
auto l116=l79;
auto l117 = convBnMish(network, weightMap, *l116->getOutput(0), 256, 1, 1, 0, 117);
ITensor* inputTensors118[] = {l117->getOutput(0), deconv115->getOutput(0)};
auto cat118 = network->addConcatenation(inputTensors118, 2);

auto l119 = convBnMish(network, weightMap, *cat118->getOutput(0), 256, 1, 1, 0, 119);
auto l120 = convBnMish(network, weightMap, *l119->getOutput(0), 256, 1, 1, 0, 120);
auto l121=l119;
auto l122 = convBnMish(network, weightMap, *l121->getOutput(0), 256, 1, 1, 0, 122);
auto l123 = convBnMish(network, weightMap, *l122->getOutput(0), 256, 3, 1, 1, 123);
auto l124 = convBnMish(network, weightMap, *l123->getOutput(0), 256, 1, 1, 0, 124);
auto l125 = convBnMish(network, weightMap, *l124->getOutput(0), 256, 3, 1, 1, 125);
ITensor* inputTensors126[] = {l125->getOutput(0), l120->getOutput(0)};
auto cat126 = network->addConcatenation(inputTensors126, 2);

auto l127 = convBnMish(network, weightMap, *cat126->getOutput(0), 256, 1, 1, 0, 127);
auto l128 = convBnMish(network, weightMap, *l127->getOutput(0), 128, 1, 1, 0, 128);

Weights deconvwts129{DataType::kFLOAT, deval, 128 * 2 * 2};
IDeconvolutionLayer* deconv129 = network->addDeconvolutionNd(*l128->getOutput(0), 128, DimsHW{2, 2}, deconvwts129, emptywts);
assert(deconv129);
deconv129->setStrideNd(DimsHW{2, 2});
deconv129->setNbGroups(128);

auto l130=l48;
auto l131 = convBnMish(network, weightMap, *l130->getOutput(0), 128, 1, 1, 0, 131);
ITensor* inputTensors132[] = {l131->getOutput(0), deconv129->getOutput(0)};
auto cat132 = network->addConcatenation(inputTensors132, 2);

auto l133 = convBnMish(network, weightMap, *cat132->getOutput(0), 128, 1, 1, 0, 133);
auto l134 = convBnMish(network, weightMap, *l133->getOutput(0), 128, 1, 1, 0, 134);
auto l135 = l133;
auto l136 = convBnMish(network, weightMap, *l135->getOutput(0), 128, 1, 1, 0, 136);
auto l137 = convBnMish(network, weightMap, *l136->getOutput(0), 128, 3, 1, 1, 137);
auto l138 = convBnMish(network, weightMap, *l137->getOutput(0), 128, 1, 1, 0, 138);
auto l139 = convBnMish(network, weightMap, *l138->getOutput(0), 128, 3, 1, 1, 139);
ITensor* inputTensors140[] = {l139->getOutput(0), l134->getOutput(0)};
auto cat140 = network->addConcatenation(inputTensors140, 2);
auto l141 = convBnMish(network, weightMap, *cat140->getOutput(0), 128, 1, 1, 0, 141);
auto l142 = convBnMish(network, weightMap, *l141->getOutput(0), 256, 3, 1, 1, 142);
IConvolutionLayer* conv143 = network->addConvolutionNd(*l142->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.143.Conv2d.weight"], weightMap["module_list.143.Conv2d.bias"]);
assert(conv143);
//144 is yolo layer
auto l145=l141;
auto l146 = convBnMish(network, weightMap, *l145->getOutput(0), 256, 3, 2, 1, 146);
ITensor* inputTensors147[] = {l146->getOutput(0), l127->getOutput(0)};
auto cat147 = network->addConcatenation(inputTensors147, 2);

auto l148 = convBnMish(network, weightMap, *cat147->getOutput(0), 256, 1, 1, 0, 148);
auto l149 = convBnMish(network, weightMap, *l148->getOutput(0), 256, 1, 1, 0, 149);
auto l150 = l148;
auto l151 = convBnMish(network, weightMap, *l150->getOutput(0), 256, 1, 1, 0, 151);
auto l152 = convBnMish(network, weightMap, *l151->getOutput(0), 256, 3, 1, 1, 152);
auto l153 = convBnMish(network, weightMap, *l152->getOutput(0), 256, 1, 1, 0, 153);
auto l154 = convBnMish(network, weightMap, *l153->getOutput(0), 256, 3, 1, 1, 154);
ITensor* inputTensors155[] = {l154->getOutput(0), l149->getOutput(0)};
auto cat155 = network->addConcatenation(inputTensors155, 2);

auto l156 = convBnMish(network, weightMap, *cat155->getOutput(0), 256, 1, 1, 0, 156);
auto l157 = convBnMish(network, weightMap, *l156->getOutput(0), 512, 3, 1, 1, 157);
IConvolutionLayer* conv158 = network->addConvolutionNd(*l157->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.158.Conv2d.weight"], weightMap["module_list.158.Conv2d.bias"]);
assert(conv158);
//159 is yolo layer
auto l160 = l156;
auto l161 = convBnMish(network, weightMap, *l160->getOutput(0), 512, 3, 2, 1, 161);
ITensor* inputTensors162[] = {l161->getOutput(0), l113->getOutput(0)};
auto cat162 = network->addConcatenation(inputTensors162, 2);

auto l163 = convBnMish(network, weightMap, *cat162->getOutput(0), 512, 1, 1, 0, 163);
auto l164 = convBnMish(network, weightMap, *l163->getOutput(0), 512, 1, 1, 0, 164);
auto l165 = l163;
auto l166 = convBnMish(network, weightMap, *l165->getOutput(0), 512, 1, 1, 0, 166);
auto l167 = convBnMish(network, weightMap, *l166->getOutput(0), 512, 3, 1, 1, 167);
auto l168 = convBnMish(network, weightMap, *l167->getOutput(0), 512, 1, 1, 0, 168);
auto l169 = convBnMish(network, weightMap, *l168->getOutput(0), 512, 3, 1, 1, 169);
ITensor* inputTensors170[] = {l169->getOutput(0), l164->getOutput(0)};
auto cat170 = network->addConcatenation(inputTensors170, 2);
auto l171 = convBnMish(network, weightMap, *cat170->getOutput(0), 512, 1, 1, 0, 171);
auto l172 = convBnMish(network, weightMap, *l171->getOutput(0), 1024, 3, 1, 1, 172);
IConvolutionLayer* conv173 = network->addConvolutionNd(*l172->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.173.Conv2d.weight"], weightMap["module_list.173.Conv2d.bias"]);
assert(conv173);
// 174 is yolo layer

auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1");
const PluginFieldCollection* pluginData = creator->getFieldNames();
IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData);
ITensor* inputTensors_yolo[] = {conv143->getOutput(0), conv158->getOutput(0), conv173->getOutput(0)};
auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj);

yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
std::cout << "set name out" << std::endl;
network->markOutput(*yolo->getOutput(0));

// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(16 * (1 << 20));  // 16MB

`

build success;

run error: set name out [12/02/2020-14:17:01] [E] [TRT] (Unnamed Layer 6) [Convolution]: kernel weights has count 2048 but 4096 was expected [12/02/2020-14:17:01] [E] [TRT] (Unnamed Layer 6) [Convolution]: count of 2048 weights in kernel, but kernel dimensions (1,1) with 64 input channels, 64 output channels and 1 groups were specified. Expected Weights count is 64 11 64 / 1 = 4096 [12/02/2020-14:17:01] [E] [TRT] Could not compute dimensions for (Unnamed Layer 6) [Convolution]_output, because the network is not valid [12/02/2020-14:17:01] [E] [TRT] Network validation failed. build out scaled_yolov4:/tensorrtx/yolov4_scaled/scaled_yolov4.cpp:532: void APIToModel(unsigned int, nvinfer1::IHostMemory**): Assertion `engine != nullptr' failed.

Thanks~

wang-xinyu commented 3 years ago

[Convolution]: kernel weights has count 2048 but 4096 was expected it means your layer setup is not right.

one debug method is set output on shadow layers, and comment out following layers. for example, you can set the 3rd layer as output, and check if running fine. and then check 6th layer, then 9th layer.

Adarine99 commented 3 years ago

[Convolution]: kernel weights has count 2048 but 4096 was expected it means your layer setup is not right.

one debug method is set output on shadow layers, and comment out following layers. for example, you can set the 3rd layer as output, and check if running fine. and then check 6th layer, then 9th layer.

got it! Thanks