yolo alternatives

2017-08-30 09:12:46 +00:00
parent 747fddab3f
commit a26ef98d2d
11 changed files with 1288 additions and 34 deletions
@@ -0,0 +1,139 @@
+[net]
+ Training
+ batch=64
+ subdivisions=8
+# Testing
+# batch=1
+# subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+#[maxpool]
+#size=2
+#stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=425
+activation=linear
+
+[region]
+anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
+bias_match=1
+classes=80
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=0
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1
@@ -0,0 +1,93 @@
+#include<iostream>
+#include "tkdnn.h"
+
+const char *input_bin  = "../tests/yolo_tiny/layers/input.bin";
+const char *c0_bin     = "../tests/yolo_tiny/layers/c0.bin";
+const char *c2_bin     = "../tests/yolo_tiny/layers/c2.bin";
+const char *c4_bin     = "../tests/yolo_tiny/layers/c4.bin";
+const char *c5_bin     = "../tests/yolo_tiny/layers/c5.bin";
+const char *c6_bin     = "../tests/yolo_tiny/layers/c6.bin";
+const char *c8_bin     = "../tests/yolo_tiny/layers/c8.bin";
+const char *c10_bin    = "../tests/yolo_tiny/layers/c10.bin";
+const char *c11_bin    = "../tests/yolo_tiny/layers/c11.bin";
+const char *c12_bin    = "../tests/yolo_tiny/layers/c12.bin";
+const char *c13_bin    = "../tests/yolo_tiny/layers/c13.bin";
+const char *g14_bin    = "../tests/yolo_tiny/layers/g14.bin";
+const char *output_bin = "../tests/yolo_tiny/layers/output.bin";
+
+int main() {
+
+    // Network layout
+    tkDNN::dataDim_t dim(1, 3, 416, 416, 1);
+    tkDNN::Network net(dim);
+
+    tkDNN::Conv2d     c0 (&net, 16, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c2 (&net, 32, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c4 (&net, 64, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p5 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p7(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c8(&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tkDNN::Activation a8(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p9(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
+    tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
+
+    tkDNN::Conv2d     c11(&net, 1024, 3, 3, 1, 1, 1, 1, c11_bin, true);
+    tkDNN::Activation a11(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
+    tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c13(&net, 425, 1, 1, 1, 1, 0, 0, c13_bin, false);
+    tkDNN::Region     g14(&net, 80, 4, 5);
+
+    // Load input
+    dnnType *data;
+    dnnType *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+
+    //print network model
+    net.print();
+
+    //convert network to tensorRT
+    tkDNN::NetworkRT netRT(&net, "yolo_tiny.rt");
+
+    dnnType *out_data, *out_data2; // cudnn output, tensorRT output
+
+    tkDNN::dataDim_t dim1 = dim; //input dim
+    printCenteredTitle(" CUDNN inference ", '=', 30); {
+        dim1.print();
+        TIMER_START
+        out_data = net.infer(dim1, data);    
+        TIMER_STOP
+        dim1.print();   
+    }
+ 
+    tkDNN::dataDim_t dim2 = dim;
+    printCenteredTitle(" TENSORRT inference ", '=', 30); {
+        dim2.print();
+        TIMER_START
+        out_data2 = netRT.infer(dim2, data);
+        TIMER_STOP
+        dim2.print();
+    }
+
+    printCenteredTitle(" CHECK RESULTS ", '=', 30);
+    dnnType *out, *out_h;
+    int out_dim = net.getOutputDim().tot();
+    readBinaryFile(output_bin, out_dim, &out_h, &out);
+    std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
+    std::cout<<"TRT   vs correct"; checkResult(out_dim, out_data2, out);
+    std::cout<<"CUDNN vs TRT    "; checkResult(out_dim, out_data, out_data2);
+    return 0;
+}