mmr/tkDNN
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- ImuOdom model into class

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- Route layer input array hard copy
- fix utils
This commit is contained in:
Francesco Gatti
2020-03-09 19:44:21 +01:00
parent cd1e1c1582
commit 6fee8d5ec5
9 changed files with 202 additions and 65 deletions
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tests/imuodom/imuodom.cpp
+35 -45
View File
@@ -1,5 +1,6 @@
#include<iostream>
#include "tkdnn.h"
#include "tkDNN/ImuOdom.h"
const char *i0_bin = "../tests/imuodom/layers/input0.bin";
const char *i1_bin = "../tests/imuodom/layers/input1.bin";
@@ -18,8 +19,14 @@ const char *l1_bin = "../tests/imuodom/layers/bidirectional_4.bin";
const char *d0_bin = "../tests/imuodom/layers/dense_3.bin";
const char *d1_bin = "../tests/imuodom/layers/dense_4.bin";
int main() {
tk::dnn::ImuOdom ImuNet;
ImuNet.init("../tests/imuodom/layers/");
const int N = 19513;
// Network layout
tk::dnn::dataDim_t dim0(1, 4, 1, 100);
tk::dnn::dataDim_t dim1(1, 3, 1, 100);
@@ -28,56 +35,39 @@ int main() {
// Load input
dnnType *i0_d, *i1_d, *i2_d;
dnnType *i0_h, *i1_h, *i2_h;
readBinaryFile(i0_bin, dim0.tot(), &i0_h, &i0_d);
readBinaryFile(i1_bin, dim1.tot(), &i1_h, &i1_d);
readBinaryFile(i2_bin, dim2.tot(), &i2_h, &i2_d);
tk::dnn::Network net(dim0);
tk::dnn::Input x0 (&net, dim0, i0_d);
tk::dnn::Conv2d x0_0(&net, 128, 1, 11, 1, 1, 0, 0, c0_bin);
tk::dnn::Conv2d x0_1(&net, 128, 1, 11, 1, 1, 0, 0, c1_bin);
tk::dnn::Pooling x0_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
tk::dnn::Input x1 (&net, dim1, i1_d);
tk::dnn::Conv2d x1_0(&net, 128, 1, 11, 1, 1, 0, 0, c2_bin);
tk::dnn::Conv2d x1_1(&net, 128, 1, 11, 1, 1, 0, 0, c3_bin);
tk::dnn::Pooling x1_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
tk::dnn::Input x2 (&net, dim2, i2_d);
tk::dnn::Conv2d x2_0(&net, 128, 1, 11, 1, 1, 0, 0, c4_bin);
tk::dnn::Conv2d x2_1(&net, 128, 1, 11, 1, 1, 0, 0, c5_bin);
tk::dnn::Pooling x2_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
tk::dnn::Layer *concat_l[3] = { &x0_2, &x1_2, &x2_2 };
tk::dnn::Route concat (&net, concat_l, 3);
tk::dnn::LSTM lstm0(&net, 128, true, l0_bin);
tk::dnn::LSTM lstm1(&net, 128, false, l1_bin);
tk::dnn::Dense d0 (&net, 3, d0_bin);
tk::dnn::Layer *lstm1_l[1] = { &lstm1 };
tk::dnn::Route lstm1_link (&net, lstm1_l, 1);
tk::dnn::Dense d1 (&net, 4, d1_bin);
net.print();
readBinaryFile(i0_bin, dim0.tot()*N, &i0_h, &i0_d);
readBinaryFile(i1_bin, dim1.tot()*N, &i1_h, &i1_d);
readBinaryFile(i2_bin, dim2.tot()*N, &i2_h, &i2_d);
dnnType *data;
tk::dnn::dataDim_t dim;
TIMER_START
// Inference
data = net.infer(dim, data);
TIMER_STOP
// Print real test
std::cout<<"\n==== CHECK RESULT ====\n";
dnnType *out0, *out1;
dnnType *out0_h, *out1_h;
readBinaryFile(o0_bin, d0.output_dim.tot(), &out0_h, &out0);
readBinaryFile(o1_bin, d1.output_dim.tot(), &out1_h, &out1);
d0.output_dim.print();
checkResult(d0.output_dim.tot(), d0.dstData, out0);
d1.output_dim.print();
checkResult(d1.output_dim.tot(), d1.dstData, out1);
readBinaryFile(o0_bin, ImuNet.odim0.tot()*N, &out0_h, &out0);
readBinaryFile(o1_bin, ImuNet.odim1.tot()*N, &out1_h, &out1);
for(int i=0; i<N; i++) {
TIMER_START
// Inference
ImuNet.update(i0_h, i1_h, i2_h);
TIMER_STOP
// Print real test
printCenteredTitle( (std::string(" CHECK RESULT ") + std::to_string(i) + " ").c_str() , '=');
//ImuNet.odim0.print();
checkResult(ImuNet.odim0.tot(), out0, ImuNet.o0_d);
//ImuNet.odim1.print();
checkResult(ImuNet.odim0.tot(), out1, ImuNet.o1_d);
i0_h += ImuNet.dim0.tot();
i1_h += ImuNet.dim1.tot();
i2_h += ImuNet.dim2.tot();
out0 += ImuNet.odim0.tot();
out1 += ImuNet.odim1.tot();
}
return 0;
}
tests/imuodom/infer.py
+18 -9
View File
@@ -8,6 +8,7 @@ import os
import random
import struct
from keras.models import Sequential, Model
import pickle
def bin_write(f, data):
data = data.flatten()
@@ -24,15 +25,23 @@ if __name__ == '__main__':
print("Load model: ", "ferrariS1.hdf5")
model = load_model("ferrariS1.hdf5")
model.summary()
weights = model.get_weights()
np.random.seed(2)
x_angle = np.random.rand(1,100,4)
x_gyro = np.random.rand(1,100,3)
x_acc = np.random.rand(1,100,3)
indata = pickle.load(open("input.pk", 'rb'))
outdata = pickle.load(open("output.pk", 'rb'))
x_angle = indata[0]
x_gyro = indata[1]
x_acc = indata[2]
[yhat_delta_p, yhat_delta_q] = model.predict(indata, batch_size=1, verbose=1)
predictdata = [yhat_delta_p, yhat_delta_q]
error = outdata[0] - predictdata[0]
print("error delta_p: ", error.sum())
error = outdata[1] - predictdata[1]
print("error delta_q: ", error.sum())
[yhat_delta_p, yhat_delta_q] = model.predict([x_angle, x_gyro, x_acc], batch_size=1, verbose=1)
#layer_name = 'dense_4'
#intermediate_layer_model = Model(inputs=model.input,
@@ -45,9 +54,9 @@ if __name__ == '__main__':
x_acc = np.array([x_acc])
#intermediate_output = np.array([intermediate_output])
x_angle = x_angle.transpose(0, 3, 1, 2)
x_gyro = x_gyro.transpose(0, 3, 1, 2)
x_acc = x_acc.transpose(0, 3, 1, 2)
x_angle = x_angle.transpose(1, 3, 0, 2)
x_gyro = x_gyro.transpose(1, 3, 0, 2)
x_acc = x_acc.transpose(1, 3, 0, 2)
#intermediate_output = intermediate_output.transpose(0, 3, 1, 2)
#print("Aggregate:")
#print(intermediate_output.tolist())