Merge with master works
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com> Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
Micaela Verucchi
@@ -0,0 +1,77 @@
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#include<iostream>
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#include "tkDNN/ImuOdom.h"
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const char *i0_bin = "../tests/imuodom/layers/input0.bin";
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const char *i1_bin = "../tests/imuodom/layers/input1.bin";
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const char *i2_bin = "../tests/imuodom/layers/input2.bin";
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const char *o0_bin = "../tests/imuodom/layers/output0.bin";
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const char *o1_bin = "../tests/imuodom/layers/output1.bin";
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const char *c0_bin = "../tests/imuodom/layers/conv1d_7.bin";
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const char *c1_bin = "../tests/imuodom/layers/conv1d_8.bin";
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const char *c2_bin = "../tests/imuodom/layers/conv1d_9.bin";
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const char *c3_bin = "../tests/imuodom/layers/conv1d_10.bin";
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const char *c4_bin = "../tests/imuodom/layers/conv1d_11.bin";
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const char *c5_bin = "../tests/imuodom/layers/conv1d_12.bin";
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const char *l0_bin = "../tests/imuodom/layers/bidirectional_3.bin";
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const char *l1_bin = "../tests/imuodom/layers/bidirectional_4.bin";
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const char *d0_bin = "../tests/imuodom/layers/dense_3.bin";
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const char *d1_bin = "../tests/imuodom/layers/dense_4.bin";
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int main() {
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tk::dnn::ImuOdom ImuNet;
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ImuNet.init("../tests/imuodom/layers/");
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const int N = 19513;
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// Network layout
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tk::dnn::dataDim_t dim0(1, 4, 1, 100);
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tk::dnn::dataDim_t dim1(1, 3, 1, 100);
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tk::dnn::dataDim_t dim2(1, 3, 1, 100);
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// Load input
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dnnType *i0_d, *i1_d, *i2_d;
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dnnType *i0_h, *i1_h, *i2_h;
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readBinaryFile(i0_bin, dim0.tot()*N, &i0_h, &i0_d);
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readBinaryFile(i1_bin, dim1.tot()*N, &i1_h, &i1_d);
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readBinaryFile(i2_bin, dim2.tot()*N, &i2_h, &i2_d);
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dnnType *data;
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tk::dnn::dataDim_t dim;
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dnnType *out0, *out1;
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dnnType *out0_h, *out1_h;
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readBinaryFile(o0_bin, ImuNet.odim0.tot()*N, &out0_h, &out0);
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readBinaryFile(o1_bin, ImuNet.odim1.tot()*N, &out1_h, &out1);
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std::ofstream path("path.txt");
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for(int i=0; i<N; i++) {
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std::cout<<"i: "<<i<<"\n";
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//TIMER_START
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// Inference
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ImuNet.update(i0_h, i1_h, i2_h);
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//TIMER_STOP
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// log path
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path<<ImuNet.odomPOS(0)<<" "<<ImuNet.odomPOS(1)<<" "<< ImuNet.odomPOS(2)<<"\n";
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path.flush();
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// Print real test
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//printCenteredTitle( (std::string(" CHECK RESULT ") + std::to_string(i) + " ").c_str() , '=');
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//ImuNet.odim0.print();
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//checkResult(ImuNet.odim0.tot(), out0, ImuNet.o0_d);
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//ImuNet.odim1.print();
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//checkResult(ImuNet.odim0.tot(), out1, ImuNet.o1_d);
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i0_h += ImuNet.dim0.tot();
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i1_h += ImuNet.dim1.tot();
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i2_h += ImuNet.dim2.tot();
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out0 += ImuNet.odim0.tot();
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out1 += ImuNet.odim1.tot();
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}
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return 0;
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}
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@@ -0,0 +1,87 @@
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import keras
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from keras.models import load_model
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import keras.backend.tensorflow_backend as KTF
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import numpy as np
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import argparse
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import tensorflow as tf
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import os
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import random
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import struct
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from keras.models import Sequential, Model
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import pickle
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def bin_write(f, data):
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data = data.flatten()
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fmt = 'f'*len(data)
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bin = struct.pack(fmt, *data)
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f.write(bin)
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if __name__ == '__main__':
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print("DATA FORMAT: ", keras.backend.image_data_format())
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print("Load model: ", "ferrariS1.hdf5")
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model = load_model("ferrariS1.hdf5")
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model.summary()
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weights = model.get_weights()
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indata = pickle.load(open("input.pk", 'rb'))
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outdata = pickle.load(open("output.pk", 'rb'))
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x_angle = indata[0]
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x_gyro = indata[1]
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x_acc = indata[2]
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[yhat_delta_p, yhat_delta_q] = model.predict(indata, batch_size=1, verbose=1)
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predictdata = [yhat_delta_p, yhat_delta_q]
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error = outdata[0] - predictdata[0]
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print("error delta_p: ", error.sum())
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error = outdata[1] - predictdata[1]
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print("error delta_q: ", error.sum())
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#layer_name = 'dense_4'
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#intermediate_layer_model = Model(inputs=model.input,
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# outputs=model.get_layer(layer_name).output)
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#intermediate_output = intermediate_layer_model.predict([x_angle, x_gyro, x_acc])
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x_angle = np.array([x_angle])
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x_gyro = np.array([x_gyro])
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x_acc = np.array([x_acc])
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#intermediate_output = np.array([intermediate_output])
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x_angle = x_angle.transpose(1, 3, 0, 2)
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x_gyro = x_gyro.transpose(1, 3, 0, 2)
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x_acc = x_acc.transpose(1, 3, 0, 2)
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#intermediate_output = intermediate_output.transpose(0, 3, 1, 2)
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#print("Aggregate:")
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#print(intermediate_output.tolist())
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print("x0: ", np.shape(x_angle))
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#print("out: ",np.shape(intermediate_output))
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x_angle = np.array(x_angle.flatten(), dtype=np.float32)
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x_gyro = np.array(x_gyro.flatten(), dtype=np.float32)
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x_acc = np.array(x_acc.flatten(), dtype=np.float32)
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yhat_delta_p = np.array(yhat_delta_p.flatten(), dtype=np.float32)
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yhat_delta_q = np.array(yhat_delta_q.flatten(), dtype=np.float32)
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#intermediate_output = np.array(intermediate_output.flatten(), dtype=np.float32)
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f = open("layers/input0.bin", mode='wb')
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bin_write(f, x_angle)
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f = open("layers/input1.bin", mode='wb')
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bin_write(f, x_gyro)
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f = open("layers/input2.bin", mode='wb')
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bin_write(f, x_acc)
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f = open("layers/output0.bin", mode='wb')
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bin_write(f, yhat_delta_p)
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f = open("layers/output1.bin", mode='wb')
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bin_write(f, yhat_delta_q)
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#f = open("layers/output.bin", mode='wb')
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#bin_write(f, intermediate_output)
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@@ -134,7 +134,7 @@ const char *regression_header5 = "../tests/mobilenetv2ssd/layers/regression_head
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int main()
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{
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downloadWeightsifDoNotExist(input_bin, "../tests/mobilenetv2ssd", "https://cloud.hipert.unimore.it/s/B6mj33k7beECXsY/download");
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downloadWeightsifDoNotExist(input_bin, "../tests/mobilenetv2ssd", "https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download");
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int classes = 21;
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+38
-27
@@ -1,43 +1,54 @@
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import keras
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import numpy as np
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from keras.models import Sequential
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from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape, Lambda
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from keras.layers import Input, Dense, Activation, Flatten, Dropout, ELU, Reshape, Lambda, Conv1D
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from keras.layers import Bidirectional, CuDNNLSTM
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from keras.layers.convolutional import Convolution2D, Convolution3D
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from keras.layers.pooling import MaxPooling2D, MaxPooling3D, AveragePooling3D
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from keras.models import Sequential, Model
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from keras.layers import Cropping2D
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import keras.backend.tensorflow_backend as KTF
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import struct
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from keras.models import Sequential, Model
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def dense_model():
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model = Sequential()
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def bin_write(f, data):
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data = data.flatten()
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fmt = 'f'*len(data)
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bin = struct.pack(fmt, *data)
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f.write(bin)
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def create_model():
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x1 = Input((3, 8), name='x1')
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conv = Conv1D(4, 2)(x1)
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lstm = Bidirectional(CuDNNLSTM(5, return_sequences=True))(conv)
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lstm2 = Bidirectional(CuDNNLSTM(5, return_sequences=False))(lstm)
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model = Model([x1], [lstm2])
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model.summary()
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model.add(Reshape((10, 10, 1), input_shape=(10, 10)))
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model.add(Convolution2D(2, (4, 4), subsample=(2, 2),
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bias_initializer='random_uniform', activation="relu"))
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model.add(Convolution2D(4, (2, 2), subsample=(1, 1),
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bias_initializer='random_uniform', activation="relu"))
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model.add(Flatten())
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model.add(Dense(4, bias_initializer='random_uniform', activation="relu"))
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sgd = keras.optimizers.Adam(lr=1e-4, decay=1e-8)
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model.compile(optimizer=sgd, loss="mse")
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return model
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if __name__ == '__main__':
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print "DATA FORMAT: ", keras.backend.image_data_format()
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print ("DATA FORMAT: ", keras.backend.image_data_format())
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model = dense_model()
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model.save("net.h5")
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model = create_model()
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model.save("net.hdf5")
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grid = np.random.rand(10,10)
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X = grid[None,:,:]
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i = np.array(grid.flatten(), dtype=np.float32)
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print i
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i.tofile("input.bin", format="f")
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print "Input: ", X
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np.random.seed(2)
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x = np.random.rand(1,1,3,8)
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r = model.predict( x[0], batch_size=1)
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r = np.array([r])
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x = x.transpose(0, 3, 1, 2)
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#r = r.transpose(0, 3, 1, 2)
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print("in: ", np.shape(x))
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print("out: ", np.shape(r))
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print("output: ", r.tolist())
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x = np.array(x.flatten(), dtype=np.float32)
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f = open("input.bin", mode='wb')
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bin_write(f, x)
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r = np.array(r.flatten(), dtype=np.float32)
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f = open("output.bin", mode='wb')
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bin_write(f, r)
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r = model.predict( X, batch_size=1)
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print np.shape(r)
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print "Result: ", r
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print "Result shape: ", np.shape(r)
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r.tofile("output.bin", format="f")
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@@ -2,22 +2,21 @@
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#include "tkdnn.h"
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const char *input_bin = "../tests/simple/input.bin";
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const char *c0_bin = "../tests/simple/layers/c0.bin";
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const char *c1_bin = "../tests/simple/layers/c1.bin";
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const char *d2_bin = "../tests/simple/layers/d2.bin";
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const char *c0_bin = "../tests/simple/layers/conv1d_1.bin";
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const char *l1_bin = "../tests/simple/layers/bidirectional_1.bin";
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const char *l2_bin = "../tests/simple/layers/bidirectional_2.bin";
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const char *output_bin = "../tests/simple/output.bin";
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int main() {
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// Network layout
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tk::dnn::dataDim_t dim(1, 1, 10, 10, 1);
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tk::dnn::dataDim_t dim(1, 8, 1, 3);
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tk::dnn::Network net(dim);
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tk::dnn::Conv2d l0(&net, 2, 4, 4, 2, 2, 0, 0, c0_bin);
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tk::dnn::Activation l1(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d l2(&net, 4, 2, 2, 1, 1, 0, 0, c1_bin);
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tk::dnn::Activation l3(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Dense l5(&net, 4, d2_bin);
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tk::dnn::Activation l6(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d l0(&net, 4, 1, 2, 1, 1, 0, 0, c0_bin);
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tk::dnn::LSTM l1(&net, 5, true, l1_bin);
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tk::dnn::LSTM l2(&net, 5, false, l2_bin);
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net.print();
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net.print();
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+98
-93
@@ -5,96 +5,89 @@ import numpy as np
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import argparse
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import tensorflow as tf
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import os
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import msgpack
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import lmdb
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import random
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import struct
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from keras.models import Sequential, Model
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def export_dense(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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def bin_write(f, data):
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data = data.flatten()
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fmt = 'f'*len(data)
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bin = struct.pack(fmt, *data)
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f.write(bin)
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#input, filters
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I, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", I, C
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print "b shape: ", B
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def export_layer(name, weights, bias):
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print ("######## EXPORT", name, "LAYER ########")
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wgs = [ [ j[i] for j in weights ] for i in xrange(C) ]
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wgs = np.array(wgs, dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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print("wgs pretranpose: ", np.shape(weights))
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# convert NHWC to NCHW
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if(weights.ndim == 4):
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weights = weights.transpose(3,2,0,1)
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elif(weights.ndim == 3):
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weights = weights.transpose(2,1,0)
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elif(weights.ndim == 2):
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weights = weights.transpose(1,0)
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else:
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print("Ndim", weights.ndim)
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raise("not implemented with dim" )
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print("weights: ", np.shape(weights))
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print("bias: ", np.shape(bias))
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weights = np.array(weights.flatten(), dtype=np.float32)
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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print(len(weights) + len(bias))
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def export_conv2d(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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# height, width, input, filters
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H, W, N, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", N, C, H, W
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print "b shape: ", B
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f = open(name + ".bin", mode='wb')
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bin_write(f, weights)
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bin_write(f, bias)
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print ("WEIGHTS saved\n")
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wgs = weights.transpose()
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wgs = wgs.transpose(0, 1, 3, 2)
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print "Final shape:", np.shape(wgs)
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wgs = np.array(wgs.flatten(), dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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def export_bidir(name, params, paramsb):
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print ("######## EXPORT", name, "LAYER ########")
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f = open(name + ".bin", mode='wb')
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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def export_conv3d(name, weights, bias):
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print "######## EXPORT", name, "LAYER ########"
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print "Original weighs:"
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print weights
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print bias, "\n"
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print np.shape(weights)
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# height, width, input, thickness, filters
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H, W, T, N, C = np.shape(weights)
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B = np.shape(bias)
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print "w shape: ", T, C, H, W #thickness is number of images for cudnn
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print "b shape: ", B
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wgs = weights.transpose()
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wgs = wgs.transpose(0, 1, 4, 3, 2)
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print "Final shape:", np.shape(wgs)
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wgs = np.array(wgs.flatten(), dtype=np.float32)
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print "REPOSITIONED WEIGHTS:"
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print wgs
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bias = np.array(bias, dtype=np.float32)
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wgs.tofile(name + ".bin", format="f")
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bias.tofile(name + ".bias.bin", format="f")
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print "WEIGHTS saved\n"
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def get_session(gpu_fraction=0.5):
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gpu_options = tf.GPUOptions(allow_growth=True)
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#per_process_gpu_memory_fraction=gpu_fraction)
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return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
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print("FORWARD")
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ker = params[0]
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rec_ker = params[1]
|
||||
bias = params[2]
|
||||
print ("export kernels: ", np.shape(ker))
|
||||
units = np.shape(ker)[1] // 4
|
||||
bin_write(f, ker[:,:units])
|
||||
bin_write(f, ker[:,units:units*2])
|
||||
bin_write(f, ker[:,units*2:units*3])
|
||||
bin_write(f, ker[:,units*3:])
|
||||
print ("export recurrent kernels: ", np.shape(rec_ker))
|
||||
bin_write(f, rec_ker[:,:units])
|
||||
bin_write(f, rec_ker[:,units:units*2])
|
||||
bin_write(f, rec_ker[:,units*2:units*3])
|
||||
bin_write(f, rec_ker[:,units*3:])
|
||||
print ("export kernels: ", np.shape(ker))
|
||||
bin_write(f, bias)
|
||||
print("WEIGHTS saved\n")
|
||||
|
||||
print("BACKWARD")
|
||||
ker = paramsb[0]
|
||||
rec_ker = paramsb[1]
|
||||
bias = paramsb[2]
|
||||
print ("export kernels: ", np.shape(ker))
|
||||
units = np.shape(ker)[1] // 4
|
||||
bin_write(f, ker[:,:units])
|
||||
bin_write(f, ker[:,units:units*2])
|
||||
bin_write(f, ker[:,units*2:units*3])
|
||||
bin_write(f, ker[:,units*3:])
|
||||
print ("export recurrent kernels: ", np.shape(rec_ker))
|
||||
bin_write(f, rec_ker[:,:units])
|
||||
bin_write(f, rec_ker[:,units:units*2])
|
||||
bin_write(f, rec_ker[:,units*2:units*3])
|
||||
bin_write(f, rec_ker[:,units*3:])
|
||||
print ("export kernels: ", np.shape(ker))
|
||||
bin_write(f, bias)
|
||||
print("WEIGHTS saved\n")
|
||||
|
||||
#https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa
|
||||
if __name__ == '__main__':
|
||||
KTF.set_session(get_session())
|
||||
print("DATA FORMAT: ", keras.backend.image_data_format())
|
||||
|
||||
parser = argparse.ArgumentParser(description='KERAS WEIGHTS EXPORTER TO CUDNN')
|
||||
parser.add_argument('model',type=str,
|
||||
@@ -103,31 +96,43 @@ if __name__ == '__main__':
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
print "DATA FORMAT: ", keras.backend.image_data_format()
|
||||
print("DATA FORMAT: ", keras.backend.image_data_format())
|
||||
|
||||
print "Load model: ", args.model
|
||||
print("Load model: ", args.model)
|
||||
model = load_model(args.model)
|
||||
model.summary()
|
||||
|
||||
|
||||
weights = model.get_weights()
|
||||
|
||||
ws = np.shape(weights)
|
||||
print "Weights shape:", ws
|
||||
print("Weights shape:", ws)
|
||||
|
||||
if not os.path.exists(args.output):
|
||||
os.makedirs(args.output)
|
||||
|
||||
num = 0
|
||||
|
||||
name_num = 0
|
||||
for l in model.layers:
|
||||
name = l.name
|
||||
if name.startswith("conv3d"):
|
||||
export_conv3d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
|
||||
elif name.startswith("conv2d"):
|
||||
export_conv2d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
|
||||
elif name.startswith("dense"):
|
||||
export_dense(args.output + "/dense" + str(name_num), weights[num], weights[num+1])
|
||||
else:
|
||||
print "skip:", name, "has no weights"
|
||||
continue
|
||||
name_num += 1
|
||||
num += 2
|
||||
print("\n\nNAME: ", l.name)
|
||||
print("input: ", l.input_shape, " output: ", l.output_shape)
|
||||
wgs = l.get_weights()
|
||||
print("wgs num: ", len(wgs))
|
||||
|
||||
name = l.name
|
||||
if name.startswith("conv3d"):
|
||||
export_layer(args.output + "/" + name, wgs[0], wgs[1])
|
||||
elif name.startswith("conv2d"):
|
||||
export_layer(args.output + "/" + name, wgs[0], wgs[1])
|
||||
elif name.startswith("conv1d"):
|
||||
export_layer(args.output + "/" + name, wgs[0], wgs[1])
|
||||
elif name.startswith("dense"):
|
||||
export_layer(args.output + "/" + name, wgs[0], wgs[1])
|
||||
elif name.startswith("bidirectional"):
|
||||
wgs = l.forward_layer.get_weights()
|
||||
export_bidir(args.output + "/" + name, l.forward_layer.get_weights(), l.backward_layer.get_weights())
|
||||
else:
|
||||
print ("skip:", name, "has no weights")
|
||||
continue
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user