better tests

tests/mnist/mnist_model.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python
+# mail:    admin@9crk.com
+# author:  9crk.from China.ShenZhen
+# time:    2017-03-22
+
+import caffe
+import numpy as np
+import cv2
+import sys
+import Image
+import matplotlib.pyplot as plt
+
+model = 'lenet.prototxt';
+weights = 'lenet.caffemodel';
+net = caffe.Net(model,weights,caffe.TEST);
+caffe.set_mode_gpu()
+img = np.array(np.random.rand(28,28), dtype=np.float32)
+#revert the image,and normalize it to 0-1 range
+
+print "INPUT: ", img
+img.tofile("input.bin", format="f")
+print "SHAPE: ", np.shape(img)
+out = net.forward_all(data=np.asarray([img]))
+
+out = out[out.keys()[0]]
+print out
+print np.shape(out)
+out.tofile("output.bin", format="f")
+#print out['prob'][0]
+#print out['prob'][0].argmax()
+
+

tests/mnist/test.cpp

@@ -0,0 +1,58 @@
+#include<iostream>
+#include "tkdnn.h"
+
+const char *input_bin   = "../tests/mnist/input.bin";
+const char *c0_bin      = "../tests/mnist/layers/Convolution0.bin";
+const char *c0_bias_bin = "../tests/mnist/layers/Convolution0.bias.bin";
+const char *c1_bin      = "../tests/mnist/layers/Convolution1.bin";
+const char *c1_bias_bin = "../tests/mnist/layers/Convolution1.bias.bin";
+const char *d2_bin      = "../tests/mnist/layers/InnerProduct2.bin";
+const char *d2_bias_bin = "../tests/mnist/layers/InnerProduct2.bias.bin";
+const char *d3_bin      = "../tests/mnist/layers/InnerProduct3.bin";
+const char *d3_bias_bin = "../tests/mnist/layers/InnerProduct3.bias.bin";
+const char *output_bin   = "../tests/mnist/output.bin";
+
+int main() {
+
+    // Network layout
+    tkDNN::Network net;
+    tkDNN::dataDim_t dim(1, 1, 28, 28, 1);
+    tkDNN::Layer *l;
+    l = new tkDNN::Conv2d     (&net, dim, 20, 5, 5, 1, 1, c0_bin, c0_bias_bin);
+    l = new tkDNN::Pooling    (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    l = new tkDNN::Conv2d     (&net, l->output_dim, 50, 5, 5, 1, 1, c1_bin, c1_bias_bin);
+    l = new tkDNN::Pooling    (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    l = new tkDNN::Dense      (&net, l->output_dim, 500, d2_bin, d2_bias_bin);
+    l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
+    l = new tkDNN::Dense      (&net, l->output_dim, 10, d3_bin, d3_bias_bin);
+    l = new tkDNN::Softmax    (&net, l->output_dim);
+ 
+    // Load input
+    value_type *data;
+    value_type *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+
+    printDeviceVector(dim.tot(), data);
+    dim.print(); //print initial dimension
+    
+    TIMER_START
+
+    // Inference
+    data = net.infer(dim, data);
+    
+    TIMER_STOP
+    dim.print();   
+
+    // Print result
+    std::cout<<"\n======= RESULT =======\n";
+    printDeviceVector(dim.tot(), data);
+ 
+    // Print real test
+    std::cout<<"\n==== CHECK RESULT ====\n";
+    value_type *out;
+    value_type *out_h;
+    readBinaryFile(output_bin, dim.tot(), &out_h, &out);
+    printDeviceVector(dim.tot(), out);
+ 
+    return 0;
+}