LSTM cudnn test
This commit is contained in:
Francesco Gatti
@@ -0,0 +1,70 @@
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#include<iostream>
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#include "tkdnn.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 *output_bin = "../tests/imuodom/layers/output.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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int main() {
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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(), &i0_h, &i0_d);
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readBinaryFile(i1_bin, dim1.tot(), &i1_h, &i1_d);
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readBinaryFile(i2_bin, dim2.tot(), &i2_h, &i2_d);
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tk::dnn::Network net(dim0);
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tk::dnn::Input x0 (&net, dim0, i0_d);
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tk::dnn::Conv2d x0_0(&net, 128, 1, 11, 1, 1, 0, 0, c0_bin);
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tk::dnn::Conv2d x0_1(&net, 128, 1, 11, 1, 1, 0, 0, c1_bin);
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tk::dnn::Pooling x0_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
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tk::dnn::Input x1 (&net, dim1, i1_d);
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tk::dnn::Conv2d x1_0(&net, 128, 1, 11, 1, 1, 0, 0, c2_bin);
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tk::dnn::Conv2d x1_1(&net, 128, 1, 11, 1, 1, 0, 0, c3_bin);
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tk::dnn::Pooling x1_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
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tk::dnn::Input x2 (&net, dim2, i2_d);
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tk::dnn::Conv2d x2_0(&net, 128, 1, 11, 1, 1, 0, 0, c4_bin);
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tk::dnn::Conv2d x2_1(&net, 128, 1, 11, 1, 1, 0, 0, c5_bin);
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tk::dnn::Pooling x2_2(&net, 1, 3, 1, 3, tk::dnn::tkdnnPoolingMode_t::POOLING_MAX);
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tk::dnn::Layer *concat_l[3] = { &x0_2, &x1_2, &x2_2 };
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tk::dnn::Route concat (&net, concat_l, 3);
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tk::dnn::LSTM lstm0(&net, 128, "ciao");
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net.print();
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dnnType *data;
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tk::dnn::dataDim_t dim;
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TIMER_START
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// Inference
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data = net.infer(dim, data); dim.print();
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TIMER_STOP
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// Print real test
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std::cout<<"\n==== CHECK RESULT ====\n";
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dnnType *out;
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dnnType *out_h;
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readBinaryFile(output_bin, dim.tot(), &out_h, &out);
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checkResult(dim.tot(), data, out);
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return 0;
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}
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@@ -0,0 +1,74 @@
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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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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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x_angle = np.random.rand(1,100,4)
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x_gyro = np.random.rand(1,100,3)
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x_acc = np.random.rand(1,100,3)
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[yhat_delta_p, yhat_delta_q] = model.predict([x_angle, x_gyro, x_acc], batch_size=1, verbose=1)
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layer_name = 'bidirectional_3'
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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(0, 3, 1, 2)
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x_gyro = x_gyro.transpose(0, 3, 1, 2)
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x_acc = x_acc.transpose(0, 3, 1, 2)
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intermediate_output = intermediate_output.transpose(0, 3, 1, 2)
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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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+33
-27
@@ -1,43 +1,49 @@
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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.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((6, 16), name='x1')
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conv = Conv1D(4, 2)(x1)
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model = Model([x1], [conv])
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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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x = np.random.rand(1,1,6,16)
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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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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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@@ -2,23 +2,15 @@
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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 *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, 16, 1, 6);
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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::Flatten l4(&net);
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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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// Load input
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dnnType *data;
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+59
-94
@@ -5,96 +5,51 @@ 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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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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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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def export_bidir(name, weights):
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print ("######## EXPORT", name, "LAYER ########")
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for w in weights:
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print(np.shape(w))
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#https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa
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if __name__ == '__main__':
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KTF.set_session(get_session())
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print("DATA FORMAT: ", keras.backend.image_data_format())
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parser = argparse.ArgumentParser(description='KERAS WEIGHTS EXPORTER TO CUDNN')
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parser.add_argument('model',type=str,
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@@ -103,31 +58,41 @@ if __name__ == '__main__':
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args = parser.parse_args()
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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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print "Load model: ", args.model
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print("Load model: ", args.model)
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model = load_model(args.model)
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model.summary()
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weights = model.get_weights()
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ws = np.shape(weights)
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print "Weights shape:", ws
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print("Weights shape:", ws)
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if not os.path.exists(args.output):
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os.makedirs(args.output)
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num = 0
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name_num = 0
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for l in model.layers:
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name = l.name
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if name.startswith("conv3d"):
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export_conv3d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
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elif name.startswith("conv2d"):
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export_conv2d(args.output + "/conv" + str(name_num), weights[num], weights[num+1])
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elif name.startswith("dense"):
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export_dense(args.output + "/dense" + str(name_num), weights[num], weights[num+1])
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else:
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print "skip:", name, "has no weights"
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continue
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name_num += 1
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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"):
|
||||
export_bidir(args.output + "/" + name, wgs)
|
||||
else:
|
||||
print ("skip:", name, "has no weights")
|
||||
continue
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user