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xupengaoxupengao
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yolov5 6.0 (wang-xinyu#763)
* Add files via upload * check_file * remove invalid data * remove data
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2 files changed

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-37
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‎yolov5/common.hpp‎

Lines changed: 28 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -156,9 +156,11 @@ IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, W
156156
return scale_1;
157157
}
158158

159+
160+
159161
ILayer* convBlock(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname) {
160162
Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
161-
int p = ksize / 2;
163+
int p = ksize / 3;
162164
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ ksize, ksize }, weightMap[lname + ".conv.weight"], emptywts);
163165
assert(conv1);
164166
conv1->setStrideNd(DimsHW{ s, s });
@@ -255,6 +257,31 @@ ILayer* SPP(INetworkDefinition *network, std::map<std::string, Weights>& weightM
255257
auto cv2 = convBlock(network, weightMap, *cat->getOutput(0), c2, 1, 1, 1, lname + ".cv2");
256258
return cv2;
257259
}
260+
// SPPF
261+
ILayer* SPPF(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c1, int c2, int k, std::string lname) {
262+
int c_ = c1 / 2;
263+
auto cv1 = convBlock(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1");
264+
265+
auto pool1 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{ k, k });
266+
pool1->setPaddingNd(DimsHW{ k / 2, k / 2 });
267+
pool1->setStrideNd(DimsHW{ 1, 1 });
268+
auto pool2 = network->addPoolingNd(*pool1->getOutput(0), PoolingType::kMAX, DimsHW{ k, k });
269+
pool2->setPaddingNd(DimsHW{ k / 2, k / 2 });
270+
pool2->setStrideNd(DimsHW{ 1, 1 });
271+
auto pool3 = network->addPoolingNd(*pool2->getOutput(0), PoolingType::kMAX, DimsHW{ k, k });
272+
pool3->setPaddingNd(DimsHW{ k / 2, k / 2 });
273+
pool3->setStrideNd(DimsHW{ 1, 1 });
274+
ITensor* inputTensors[] = { cv1->getOutput(0), pool1->getOutput(0), pool2->getOutput(0), pool3->getOutput(0) };
275+
auto cat = network->addConcatenation(inputTensors, 4);
276+
auto cv2 = convBlock(network, weightMap, *cat->getOutput(0), c2, 1, 1, 1, lname + ".cv2");
277+
return cv2;
278+
}
279+
280+
//
281+
282+
283+
284+
258285

259286
std::vector<std::vector<float>> getAnchors(std::map<std::string, Weights>& weightMap, std::string lname) {
260287
std::vector<std::vector<float>> anchors;

‎yolov5/yolov5.cpp‎

Lines changed: 18 additions & 36 deletions
Original file line numberDiff line numberDiff line change
@@ -6,8 +6,8 @@
66
#include "common.hpp"
77
#include "utils.h"
88
#include "calibrator.h"
9-
10-
#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32
9+
#include <typeinfo>
10+
#define USE_FP32 // set USE_INT8 or USE_FP16 or USE_FP32
1111
#define DEVICE 0 // GPU id
1212
#define NMS_THRESH 0.4
1313
#define CONF_THRESH 0.5
@@ -35,30 +35,29 @@ static int get_depth(int x, float gd) {
3535
return std::max<int>(r, 1);
3636
}
3737

38+
39+
3840
ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
3941
INetworkDefinition* network = builder->createNetworkV2(0U);
4042

4143
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
4244
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
4345
assert(data);
44-
4546
std::map<std::string, Weights> weightMap = loadWeights(wts_name);
46-
4747
/* ------ yolov5 backbone------ */
48-
auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");
49-
auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
48+
auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
49+
assert(conv0);
50+
auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
5051
auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
5152
auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
52-
auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");
53+
auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
5354
auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
5455
auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
5556
auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
56-
auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, 9, 13, "model.8");
57-
57+
auto bottleneck_csp8 = C3(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.8");
58+
auto spp9 = SPPF(network, weightMap, *bottleneck_csp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.9");
5859
/* ------ yolov5 head ------ */
59-
auto bottleneck_csp9 = C3(network, weightMap, *spp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.9");
60-
auto conv10 = convBlock(network, weightMap, *bottleneck_csp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");
61-
60+
auto conv10 = convBlock(network, weightMap, *spp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");
6261
auto upsample11 = network->addResize(*conv10->getOutput(0));
6362
assert(upsample11);
6463
upsample11->setResizeMode(ResizeMode::kNEAREST);
@@ -76,9 +75,7 @@ ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
7675

7776
ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };
7877
auto cat16 = network->addConcatenation(inputTensors16, 2);
79-
8078
auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17");
81-
8279
/* ------ detect ------ */
8380
IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]);
8481
auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");
@@ -91,11 +88,9 @@ ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
9188
auto cat22 = network->addConcatenation(inputTensors22, 2);
9289
auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
9390
IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]);
94-
9591
auto yolo = addYoLoLayer(network, weightMap, "model.24", std::vector<IConvolutionLayer*>{det0, det1, det2});
9692
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
9793
network->markOutput(*yolo->getOutput(0));
98-
9994
// Build engine
10095
builder->setMaxBatchSize(maxBatchSize);
10196
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
@@ -124,40 +119,35 @@ ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilder
124119

125120
return engine;
126121
}
127-
122+
//v6.0
128123
ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
129124
INetworkDefinition* network = builder->createNetworkV2(0U);
130-
131125
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
132126
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
133127
assert(data);
134-
135128
std::map<std::string, Weights> weightMap = loadWeights(wts_name);
136-
137129
/* ------ yolov5 backbone------ */
138-
auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");
139-
auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
130+
auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
131+
auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
140132
auto c3_2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
141133
auto conv3 = convBlock(network, weightMap, *c3_2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
142-
auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");
134+
auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
143135
auto conv5 = convBlock(network, weightMap, *c3_4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
144136
auto c3_6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
145137
auto conv7 = convBlock(network, weightMap, *c3_6->getOutput(0), get_width(768, gw), 3, 2, 1, "model.7");
146138
auto c3_8 = C3(network, weightMap, *conv7->getOutput(0), get_width(768, gw), get_width(768, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
147139
auto conv9 = convBlock(network, weightMap, *c3_8->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.9");
148-
auto spp10 = SPP(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), 3, 5, 7, "model.10");
149-
auto c3_11 = C3(network, weightMap, *spp10->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.11");
150-
140+
auto c3_10 = C3(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.10");
141+
auto sppf11 = SPPF(network, weightMap, *c3_10->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.11");
151142
/* ------ yolov5 head ------ */
152-
auto conv12 = convBlock(network, weightMap, *c3_11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");
143+
auto conv12 = convBlock(network, weightMap, *sppf11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");
153144
auto upsample13 = network->addResize(*conv12->getOutput(0));
154145
assert(upsample13);
155146
upsample13->setResizeMode(ResizeMode::kNEAREST);
156147
upsample13->setOutputDimensions(c3_8->getOutput(0)->getDimensions());
157148
ITensor* inputTensors14[] = { upsample13->getOutput(0), c3_8->getOutput(0) };
158149
auto cat14 = network->addConcatenation(inputTensors14, 2);
159150
auto c3_15 = C3(network, weightMap, *cat14->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.15");
160-
161151
auto conv16 = convBlock(network, weightMap, *c3_15->getOutput(0), get_width(512, gw), 1, 1, 1, "model.16");
162152
auto upsample17 = network->addResize(*conv16->getOutput(0));
163153
assert(upsample17);
@@ -166,7 +156,6 @@ ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuil
166156
ITensor* inputTensors18[] = { upsample17->getOutput(0), c3_6->getOutput(0) };
167157
auto cat18 = network->addConcatenation(inputTensors18, 2);
168158
auto c3_19 = C3(network, weightMap, *cat18->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.19");
169-
170159
auto conv20 = convBlock(network, weightMap, *c3_19->getOutput(0), get_width(256, gw), 1, 1, 1, "model.20");
171160
auto upsample21 = network->addResize(*conv20->getOutput(0));
172161
assert(upsample21);
@@ -175,22 +164,18 @@ ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuil
175164
ITensor* inputTensors21[] = { upsample21->getOutput(0), c3_4->getOutput(0) };
176165
auto cat22 = network->addConcatenation(inputTensors21, 2);
177166
auto c3_23 = C3(network, weightMap, *cat22->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
178-
179167
auto conv24 = convBlock(network, weightMap, *c3_23->getOutput(0), get_width(256, gw), 3, 2, 1, "model.24");
180168
ITensor* inputTensors25[] = { conv24->getOutput(0), conv20->getOutput(0) };
181169
auto cat25 = network->addConcatenation(inputTensors25, 2);
182170
auto c3_26 = C3(network, weightMap, *cat25->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.26");
183-
184171
auto conv27 = convBlock(network, weightMap, *c3_26->getOutput(0), get_width(512, gw), 3, 2, 1, "model.27");
185172
ITensor* inputTensors28[] = { conv27->getOutput(0), conv16->getOutput(0) };
186173
auto cat28 = network->addConcatenation(inputTensors28, 2);
187174
auto c3_29 = C3(network, weightMap, *cat28->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.29");
188-
189175
auto conv30 = convBlock(network, weightMap, *c3_29->getOutput(0), get_width(768, gw), 3, 2, 1, "model.30");
190176
ITensor* inputTensors31[] = { conv30->getOutput(0), conv12->getOutput(0) };
191177
auto cat31 = network->addConcatenation(inputTensors31, 2);
192178
auto c3_32 = C3(network, weightMap, *cat31->getOutput(0), get_width(2048, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.32");
193-
194179
/* ------ detect ------ */
195180
IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.0.weight"], weightMap["model.33.m.0.bias"]);
196181
IConvolutionLayer* det1 = network->addConvolutionNd(*c3_26->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.1.weight"], weightMap["model.33.m.1.bias"]);
@@ -200,7 +185,6 @@ ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuil
200185
auto yolo = addYoLoLayer(network, weightMap, "model.33", std::vector<IConvolutionLayer*>{det0, det1, det2, det3});
201186
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
202187
network->markOutput(*yolo->getOutput(0));
203-
204188
// Build engine
205189
builder->setMaxBatchSize(maxBatchSize);
206190
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
@@ -217,10 +201,8 @@ ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuil
217201
std::cout << "Building engine, please wait for a while..." << std::endl;
218202
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
219203
std::cout << "Build engine successfully!" << std::endl;
220-
221204
// Don't need the network any more
222205
network->destroy();
223-
224206
// Release host memory
225207
for (auto& mem : weightMap)
226208
{

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